SmartDJ: Declarative Audio Editing with Audio Language Model
Authors: Zitong Lan
Yiduo Hao
Mingmin Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: Audio editing
Latent diffusion model
Audio language model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Audio editing plays a crucial role in VR/AR immersion, virtual conferencing, sound design, and  interactive media. 
However, recent generative audio editing models depend on template-like instruction formats and are restricted to mono-channel audio.
Moreover, existing systems require users to specify low-level editing actions, rather than expressing the desired outcome at a higher semantic level.
We introduce SmartDJ, a novel framework for stereo audio editing that enables declarative audio editing, where the users describe the desired outcome while delegating the underlying editing operations to the system.
Given a high-level instruction, SmartDJ decomposes it into a sequence of atomic edit operations, such as adding, removing, or spatially relocating sound events.
These operations are then executed by a diffusion model trained to edit stereo audio. 
To enable this capability, we design a scalable data synthesis pipeline that produces paired examples of declarative instructions, atomic edit operations, and audios before and after each edit operation. 
Experiments demonstrate that SmartDJ achieves superior perceptual quality, spatial realism, and semantic alignment compared to prior audio editing methods.

Published as a conference paper at ICLR 2026
SMARTDJ: DECLARATIVE AUDIO EDITING WITH AUDIO LANGUAGE MODEL
Zitong Lan, Yiduo Hao, Mingmin Zhao
University of Pennsylvania
https::/waat seas. , upenn .
ABSTRACT
Audio editing plays a crucial role in VR/RR immersion, virtual conferencing, sound design, and interactive media. However, recent generative audio editing models depend on template-like instruction formats and are restricted to mono- channel audio. Moreover, existing systems require users specify low-level editing actions, rather than expressing the desired outcome at a higher semantic level. We introduce SmartDJ, a novel framework for stereo audio editing that enables declarative audio editing. where the users describe the desired outcome while delegating the underlying editing operations to the system. Given a high- level instruction, SmartDJ decomposes it into a sequence of atomic edit operations, such as adding, removing, or spatially relocating sound events. These operations are then executed by diffusion model trained to edit stereo audio. To enable this capability, we design a scalable data synthesis pipeline that produces paired examples of declarative instructions, atomic edit operations, and audios before and after each edit operation. Experiments demonstrate that SmartDJ achieves superior perceptual quality, spatial realism, and semantic alignment compared to prior audio editing methods.
INTRODUCTION
Imagine you recorded sound in forest on rainy day and wanted to transform it into the soundscape of a sunny forest. Achieving this transformation requires many edits: removing elements like rainfall and adding new effects such leaf rustling. Traditionally, audio editing is procedural process: the user specifies how to achieve the goal, step by step, by removing rain, layering new samples, adjusting gain, and so on. Yet in practice, users would prefer just to issue a single, declarative instruction, e.g.. "make this sound like a sunny day", and rely on an intelligent audio editor to decide what edits are needed. We call this declarative editing: the user declares what the desired outcome should be, while leaving the how, i.e, the sequence of operations, to the system. Such a declarative paradigm would unlock wide range of applications in VR/AR, gaming, cinematic post- post-production, and beyond, where designing and modifying audio scenes is central to immersive experiences.
Recent advances in deep generative models have opened exciting possibilities for text-to-audio generation and language-driven editing (Evans et al., 2024a bb) Hai et al.] [0024] Heydari et al. 2024
Huang et al. [203aa] Liu et al., 2023a [2024a] Jia al. [0224] Liang et al. 2024 Wang al. 2023
Xu et al. 2024). However, existing audio editors remain limited in two key aspects. First, they rely on templated instructions such as "add the sound of birds" or "remove the sound of rain", which restricts their ability to handle declarative or abstract user instructions. Second, they operate only on monaural ssigle-channed audio, discarding spatial features such as interaural time and level differences, which are primary cues for spatial hearing. Without these cues, even semantically correct edits sound flat and fail to deliver immersive experiences.
In contrast, declarative editing requires an editor to bridge from declarative goals to detailed operations automatically. When a user issues instructions such as "place me in a concert hall" or "have this audio in library". the system must reason about which sounds to remove, which to preserve, how to adjust loudness, when to introduce new events, and how to shift spatial position. Current audio editors based diffusion models cannot achieve this, as they lack the reasoning capacity to interpret these declarative instructions. On the other hand, language models alone are also insufficient: while they can parse the user's request, they lack grounding in the audio itself, making it impossible to

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"Have this oudio in sunny forest"
Step 1: Remove roining
Step Add tree leoves bustling
Cat meowing, Roining
Audio events:
8
Planner
Stop Add chirping
Step
Step n
LDM Editor
LDM
Original stereo audio
SmartD)
Editor
Edited stereo audio
Figure I: SJartDJ has an Audio Language Model (ALM) that acts as an edit planner to decompose the instructions into atomic steps, guiding the Latent Diffusion Model (LDM) editor produce high-quality edited audio.
decide which sound elements should be suppressed, emphasized, or retained. This gap highlights the need for framework that jointly reasons over language and audio, combining natural-language instruction understanding with audio-aware analysis and editing.
In this work, we present SmartDJ, the first framework for declarative audio editing by introducing Audio Language Models (ALM) into the audio editing loop. This approach is motivated by the recent success of MLLMs in multimodal grounding and reasoning (Chu et al. 2023 Ghosh et al. 2025a Kong et al. the ALM act planner: perceives the original audio while interpreting the user's declarative instruction and decomposes into a sequence of atomic edit operations, such as adding or removing sound, shifting direction, or adjusting volume, etc. These atomic edit operations are then executed sequentially by conditional Latent Diffusion Model (LDM) as an audio editor, realizing the user's declarative goal. This separation of planning and editing transforms audio editing from a procedural task into a declarative one. Moreover, because the intermediate representation is expressed in natural language, users can easily inspect, refine, or override the planned edits interactively.
2024a Liu et al.) 2023b Li et al. 2023a). As illustrate in Fig. 1 our key idea is to let
Training system to perform declarative editing introduces a fundamental challenge: it requires paired examples of declarative instructions, their corresponding atomic edit sequences, and the before-and-after edited audio. Such data is rarely available, as natural soundscapes are complex and difficult to edit at scale. To address this, we construct a scalable data generation pipeline that provides controllable audio scenes. Each scene is assembled from independently parameterized sound events with attributes such as direction and loudness. Within this pipeline, an off-the-shelf LLM acts as the designer, producing diverse declarative editing instructions and the corresponding atomic operations. Audio signal processing then serves as the composer, rendering each operation by adjusting or mixing sound events to produce the corresponding audio after every edit. Together, this designer-compost pipeline mirrors the planner-editor structure of our model. It produces a large-scale corpus of realistic editing operations, providing the supervision needed to train and evaluate our model.
Experimental results show that SJartDJ delivers superior editing quality and better alignment with declarative user instructions from both subjective metries and human evaluations. Our LDM also outperforms existing baselines for audio editing. Ablation studies show that our ALM can effectively reason about and decompose declarative user instructions into a sequence of editing actions.
In summary, our main contributions are as follows:
We introduce SmartDJ, the first stereo audio editor capable of interpreting declarative user instruc- tions with an ALM and executing them as precise atomic edit operations through an LDM
We introduce the first scalable pipeline for generating editable stereo audio scenes, combining declarative instructions with controllable events enable reasoning- based audio editors.
We conduct extensive experiments and user studies with different baseline methods and demonstrate that SmartDJ achieves the highest editing quality for both objective and subjective metrics.
RELATED WORK
Audio generation and editing. With the current advances in deep generative models, lots of methods
have achieved high-quality audio generation from text and multi-modal conditions (Chen et al. 2024
Evans et al. 2024a Hai et al. 2024; Huang et al. 2023a Liu et al. 2023a| 2024a Wang et al. 2023). Recently, spatial audio generation has attracted more attention (Evans et al. 2024a Heydan et al.)
2024 Liu et al. 2025 Sun et al. 2024), providing more realistic and immersive listening experiences.
Parallel to these generation efforts, text-guided audio editing also emerged as powerful tool for
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modifying existing audio recordings. Audit (Wang et al. 2023) introduced end-to-end diffusion model conditioned on both the input audio and simple, structured text commands, but its reliance on fixed editing templates limits flexibility to interpret declarative user prompts. WavCraft (Ling et al. 2024) leverages GPT-api to parse user instructions, yet it expects fully specified prompts (e.g.. "extract baby crying from the audio" or "apply low-pass filter the wave crashing sound and add it back"). Some recent work (Jia et al. (2024; Manor& & Michaeli 2024; Xu et al., (024)) adapts image-editing techniques (e.g., DDPM inversion, Null-text inversions, attention map manipulation) to monaural audio. They require precise token-level modification to complete the edit, struggle with declarative instructions. To summarize, none of the existing work can interpret declarative user imput to complete the audio editing. Besides, existing frameworks remain confined to monaural outputs and are ill-suited for immersive spatial scenarios Lan et al. (2024) 2025a b).
Multimodal Large Language Model. Large language models (LLMs) are remarkable in natural language processing tasks. Provided with multimodal inputs, such as images and audio, multi-
Kong et al.) 2024b Ghosh et al. 2025b) demonstrate exceptional performance across a wide range of down- stream risual-language and audio-language tasks. In the vision domain, LLaVA VA (Liu et al. 2023b)
modal LLMs (MLLMs) (et al. 2022. Liu et al. 2023b Li et al. 2023a Team 2025
enables LLMs to achieve ceneral-purpost visual and language understanding by fine-tuning on a
dataset. In the audio domain, LTU (Gong et al. (023) and Audio Flamingo (Kong et al. [024b] GGosh et al., (025b) enhance L.Ms with the ability to process non- speech sounds and nen-verbal speech. With strong capabilities of MLLMs, researchers introduced them into the field of visual content generation (Wu et al. [024cc Xia et al. [2023] Wu et al. 2024b)
multimodal cestructioo-followiii
Koh et al. 2023 Fu al. 2024 Huang et al., (202bb) world modeling (Wu al. [2024a] Ge et al.
2024 Mai et al., 2024), grounding (Lai et al. 2024 Hao et al.
2024 Cheng et al., 2024). and
embodied AI (et. all 2023 et al. 2023b Mu al.
2023 Szot al.
2024| Wu et al. (002)). In
image generation and editing, various works (Koh et al., 2023 Fu et al.
2024 Huang et al. (002bb)
use VLM to guide diffusion models. However, in the audio domain, existing methods have not exploited the reasoning capabilities of audio language models.
Diflusion-bassed Image Editing, In contrast to text-to-image generation, image editing focuses on altering specific elements attributes within an image while preserving the contents of the remaining image. Diffusion models have been widely used in image editing tasks (Couairon et al. 2022 Hertz
et al. 2022] Hui et al. 2024) by altering the inversion process, which produces latent representation that can reconstruct the image through the generative process. SDEdit (Meng et al.) 2022) first adds noise to the source image, and then subsequently denoises the image through the SDE produce the target image. P2P (Hertz et al.) 2022) adjusts the cross-attention features according to the difference between the source and target captions to generate the target images. Based on this. IP2P (Brooks et al. 2023) finetuned diffusion model on edit image triplets to enable image editing with simple natural language instructions. Following works (Geng et al. 2023 Hui et al. 2024) further scale up the dataset to support more capable and generalized models. Furthermore, later works (Fu et al., 2024 Huang et al. 2023b) use vision-language models to guide diffusion models for image editing.
3 METHOD
In this section, we first define the task and notations. We then introduce our proposed framework SmartDJ that combines an Audio Language Model for interpreting declarative editing instructions with a diffusion model for executing sequential audio edits. Finally, we describe a scalable data generation pipeline powered by LLMs to support supervised training and evaluation.
3.1 PROBLEM DEFINITION AND NOTATIONS
Let ag denote the original audio waveform, which contains multiple audio events (c.g., cat meowing. rainfall), as shown in Fig. D We define declarative editing instruction P as a natural language description of desired transformation of the overall audio scene. Such instructions are typically declarative: they specify what the desired outcome should be (e.g., "make it sound like a quier morning in a sunny forest" or "transform this into an indoor library setting"), but do not explicitly prescribe the individual operations. Since P is declarative instruction, it must be decomposed into detailed sequence of atomic editing steps S = {$1, $2,... .2......p, sn}, where each step 8; either modifies an existing audio event in ag introduces new event needed to fulfill P. We denote a;(i = 1, .... n)
as the intermediate audio after applying step st, where ag is the original input and a, is the final edited audio.
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Specifically, each step 8; either modifies existing audio event introduces event required to satisfy P. The atomic editing operations considered this work are:
Add Mix new sound event into the scene (c.g., inject bird chirps).
Remove: Delete an existing sound event (e.g., remove car engine noise).
Extract: Isolate a particular sound event from the original audio while removing others.
Volume adjust: Adjust the volume of a specific event.
Change direction: Modify the spatial location of an event.
Time shift: Adjust the sound timing by x seconds.
Add reverberation Add reverheration
single source.
Timbre adjust:
Modify the timbre of a single sound source.
The goal of the editing is to produce a target edited audio clip by applying the sequence of edits to ag. Importantly, this editing formulation should preclude shortcut solutions that ignore the original input (e.g., re-generating an entirely new audio from scratch). Instead, the edited audio must preserve unedited content from ag while achieving the requested audio scene transformation. Please refer to Appendix A.3 for more details.
3.2 SMARTDJ FRAMEWORK
SmartDJ consists of an Audio Language Model (ALM) and a Latent Diffusion Model (LDM). We leverage the ALM planner to interpret declarative instructions and generate sequence of atomic editing steps. The LDM editor then executes these steps sequentially to transform the original audio. As illus- trated in Fig- the ALM takes the original audio dg and the declarative editing instruction P input, and outputs a sequence of atomic editing steps S = {s1, $2, SnJ.
High-level instruction
ALM
Original audio
These editing steps are then executed sequentially by the LDM, producing intermediate results 41, ........ Ons
Atomic edit steps
Step
where on the final edited audio. The overall process is formulated as:
LDM
LDM
LDM
4
{ = (LM( P)
Figure SmartDJ framework overview
a, LD= LD= (4
3.3 AUDIO LANGUAGE MODEL FOR ATOMIC EDITING STEPS GENERATION
The Audio Language Model (ALM) takes input the original audio clip and the declarative editing instruction and generates scquence of atomic editing steps. As shown at the top of Fig. 3 we first encode dg using pretrained audio encoder (i.e., CLAP (Wu et al. 2023)) to obtain audio embedding ža, which is injected into the ALM via adapter layers. In parallel, the instruction P is tokenized and encoded context for the ALM.
sequence of embeddings (p1, . pk)), which serve as the textual
Our ALM trained in an auto-regressive manner to generate the token sequence corresponding the atomic editing steps S, by minimizing the following objective:
CALM = Σ log Po(r(=r
Zu. T1:1-11111111 P1:k),
where and r' are the ground truth and predicted text tokens for the atomic editing steps S, 7 is the length of the tokens, and 0 denotes the model parameters. To enable efficient fine-tuning, we freeze the parameters of the CLAP audio encoder, apply Low-Rank Adaptation (LoRA) (Hu et al.) 2022) to a small subset of the LLM layers(Ghosh et al. 20255), and fully fine-tune the adapter layers.
Separate training. We train the ALM and LDM independent modules rather than end-to-end. This enables tuman-in-the-loop editing. where users can casily intervene at the level generated natural- hetural-lagguage-baee based atomic steps before LDM editor inference. Besides, it makes training and computation more efficient and promotes modularity. This design allows for different ALMs or LDMss to be swapped in out with minimal re-training effort, making it both practical and extensible for diverse editing scenarios.
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High-level oudio sounds sunny
ALM training
LoRa
LLLM
Stop 1: Remove engline
Audio * Encoder
Adapter
Large Language Model
Step 2: Add aceon waves et left by ZaB Step ni Add people chatter at right by 2db
Inference only: 1 to Step
Original
Nokey wttett
L.DMM
LDM training
VAE Encoder
Diffusion Transformer
00O latent
VAE Encoder
Original lotent
xn Step
Edited audio
Trainable params
Freeze params
Forward Process
Training Loss
Inference
Figure 3: Sm framework. Our method incorporates ALM edit planner that understands both the original audio and the declarative instructions to produce atomic edit steps. These atomic steps are then fed into an LDM editor to edit the audio sequentially. The ALM and LDM modules are trained separately.
3.4 SEQUENTIAL STEREO AUDIO EDITING WITH LATENT DIFFUSION MODEL
The Latent Diffusion Model (LDM) in our framework performs audio editing conditioned on the atomic editing steps S. To support this, adopt latent diffusion architecture (Hai et al. 2024 Rombach et al. 2022) and extend to enable editing of stereo audio with spatial effects.
Stereo Audio VAE. Given stereo audio signal E R where L is the number of time-domain samples at two audio channels (left and right). The audio Variational Autoencoder (VAE) encodes into a latent representation à E RCXL' where C and T.' denote the number of latent channels and the temporal length of the latent sequence. Similar to DAC (Kumar et al. 2023) and Stable-Audio- Open (Evans et al., 2024b), our audio VAE is based on a ID-CNN autoencoder with continuous VAE bottleneck and snake activation functions. The resulting latent has dimension of C 128 and length of L' = L/480, resulting compression ratio of 7.5x.
Latent Diffusion Model: Our diffusion model conditions on both the text description s; at the i-th editing step and the latent representation of the audio from the previous step. å;-1, to generate the updated latent à, We use the FLANNTS (Chung al., 2024) text encoder Freu () convert 8; to text embeddings. At each editing step, we initialize a randomly noised latent á& E IR COX' which is concatenated with 0;-1 to form the input (a,-1; ac] E 1R2CxL' to the diffusion model. The model is conditioned on (s;) via cross-attention layers, and the diffusion timestep ł is incorporated through modified AdaLN module (Hai et al. (2024) to reduce model parameters. We implement Diffusion Transformer (DiT) that learns to denoise the latent á' across multiple timesteps by predicting the added noise. Let denote the true added Gaussian noise, and let cØ(-) be the predicted noise output by the model. The training objective is to minimize the following denoising loss:
fLOM ( 1.. ||c co Frex((s). [a 4[1I1
During inference, we use DDIM sampling (Song al. 2021) with classifier guidance (CFG), which has proven effective for text-guided generation and editing (Ho & Salimans (022). CFG steers the denoising process by interpolating between conditional and unconditional model predictions:
Eo = w ca Emsi l at) + (1 w) (t Ø, [6:-1: a(1)
(5)
where w is the guidance scale and Ø denotes the text embedding an empty string.
3.5 DECLARATIVE INSTRUCTION AUDIO EDITING DATASET CURATION
Since no public dataset features audio editing conditioned on declarative instructions, we develop a scalable data generation pipeline, as illustrated in Fig. 4p. For each data point, we first randomly sample K single-event audio clips from public datasets, each labeled with tags such ("car engine", "bell ring", "goat bleat",...) ...). We feed these labels into GPT-4o and prompt to act as sound designer: it designs a declarative editing instruction P that transforms the original mix into a new audio scene (e.g.. "Make this sound like a countryside morning" or "Make it sound like busy train station on sunny afternoon"). It then decomposes P into sequence of atomic
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Original audio
Car engine
Bell ring
Goat bleat
dg
bus
Ready
Livelys
Fores
"R day eeeiing a St Fet Wr
"Make this sound like
Composer
country ddde dide
countryside marning"
222 lond
GPT-40 Designer
Atomic edit steps
Car engine | x
cons sogge ton Poookaaaa kahop Mirport
Step 1: Remove the car engine sound
01
race pescert fomily
Rooster crowing |||l)-ni@ @300
party
Key word in the dataset
Step 2: Add rooster crowing right
|
02
Add
Extract
2%
Church bell ring -I[I0] [||u||[x0.5
Dieccion
0.2% Reverb
Step Turn down church bell ring
On
Timbre
6.4%
6.5%
Edited audio
10
20
a) Dataset synthesis pipeline
c) Composition of operations
Figure 4: Scalable data synthesis. a) pipeline: sample audio clips from databases with text labels and compose them into original audio ag: These text labels are then fed into GPT-4o, which is prompted to design declarative instruction P and generate corresponding atomic steps S. We compose the target audio a1, .2,..., following the atomic steps sequentially with rule-based composer. b) Key words in the declarative instruction. c) The proportion of each single-step edit operation in the dataset.
edits {s1. $2, ...: 3, including both add operations and modifications existing events (e.g.. remove, turn up/down, change sound direction. ion).
Once the sound designer has provided the sequential edit operations, an audio signal processing based composer takes over. To generate edit audio pairs, the composer first synthesizes the initial audio do by superposing the audio clips. Spatial effects are rendered with direction-dependent phase and amplitude on two channels. For each atomic edit s;, we proceed as follows:
8; modifies an existing event, we update its volume level or sound direction.
s; an add operation, we retrieve a new clip from the database with a matching label.
Since each sampled audio event is independently editable, edit step s; that modifies existing event can be converted into event-level parameter adjustments, without altering any other sources in the mixture a;-1- I. For example, simulate "remove the car engine sound" (Fig. 4). set the engine's 's volume to zero to generate 01. To simulate "add rooster crowing at right", we retrieve rooster clip, apply the specified spatial effect, and superpose on a1 to obtain ag. To simulate "turn down the dog bark", we reduce the volume of the corresponding clip. This allows us to generate a complete editing trajectory a1; O2,..., On by progressively updating event-level parameters and re-composing the audio scene. Our resulting dataset contains declarative editing instructions, atomic edit sequences, and the audios for the full editing trajectory. More details available in Appx. A.4
4 EXPERIMENT
4.1 SETUP
Dataset. We use combination of datasets including AudioCaps (Kim et al. 2019). VGGSound (Chen
al. 2020), FSD50k (Fonseca et al., 202T), ESC50 (Piczak, 2015). and WavCaps Mei et al. 2024).
We adopt dataset cleaning pipelines tollowing prior work (Hai et al. 2024 Sun et al. 2024
Wang
al. 2023). Each audio is trimmed or padded into 10 seconds with a sampling rate of 24 K. We
sample 2-5 audio events and use GPT-4o to create 240k training pairs and 2k evaluation pairs of declarative audio editing data (from test set of AudioCaps) to train ALM planner and evaluate the whole editing pipeline. We present the keyword in the declarative instructions in Fig. 4b. We also expand the size of single-step editing data pairs (St, Qc-1, 0z) to IM. Fig. 4 shows the proportion of each operation. This dataset is to train audio editor. We use 3k single-step pairs from AudioCaps test set to evaluate the results. More details in Appx.[A.]
Spatial Sound. We use interaural distance of 17 and adopts a cardioid directivity gain pattern to model the sound level differences between left/right ear when sound events are coming from different directions. To introduce room realism, further use PyRoom Acoustis (Scheibler et al. 2018)

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M
Audio contents: baby engine
Audio contents: people bird singing
Audio contents: clock tik people whistle
Moke this sound like inside busy family home
Make it sound like recurded night outside
Have this sound like in Nirory
Atomic edit steps:
Atomic edit steps:
Atomic edit steps:
Remove engine roor
Remove people snore
Remove person whistle
Add vacuum cleaner at left by 3d0
Add crichets at left by 11d8 Add light wind right by odi
Turn up clock tick by 2d8
Add kitchen sounds right by
Addi pages turning at by 2d8
Figure 5:
Examples of ALM's output detailed steps. Our ALM module identifies events in the original audio clips and reasons on the given declarative instruction produce aligned editing steps.
to simulate room impulse response with reverberation time (RT60) (Sun et al., 2024) ranging from 0.3 s 1.2 s. Room size, microphone orientation, and source/mic placement are randomized during synthesis. More details on acoustic simulation can be found in Appx. A.2
Metrics. To evaluate edit quality and diversity, we use common metrics in audio generation and editing (Liu et al., 2023a Wang et al., 2023), including Fréchet Distance (FD), Kullback-Leini divergence (KL), Fréchet Audio Distance (FAD), Inception Score (IS), and Log-Spectral Distance (LSD). We use CLAP score to measure the semantic similarities between the edited audio and the text prompt. For spatial audio, we calculate GCC MSE (GCC) based on Generalized Cross-Correlate Correlation with Phase Transform (GCC-PHAT), and use StereoCRW (Chen et al. (022)) to produce stereo audio features to evaluate CRW MSE (CRW) and Fréchet Stereo Audio Distance (FSAD).
Baselines. To evaluate the declarative instruction based audio editing task, we first train end- to-end version of Audit (Wang et al.] 2023) that directly predicts the final-step edited audio a, conditioned the original audio a, and declarative instruction P in one step without ALM. We extend the mono-channel Audit to our binaural setting, where we stack the left and right channels of the mel-spectrograms as the model inputs. We also evaluate various zero-shot and training-required editing methods based on the ALM's outputs to perform multi-step sequential editing. The zero-shot methods include SDEdit (Meng et al. 2022)) DDIM Inversion (Mokady et al.) 2022). ZETA (Manor & Michaeli 2024), and AudioEditor (Jia et al. 2024). For fair comparison, we replace these methods' generation backbone with the current SOTA methods.
AudioEditor, we replace Affusion with BEWO (Sun et al.] (022)) to support binaural editing. In SDEdit, DDIM Inversion, and ZETA, use Stable-Audio-Oper (Evans et al.) 2024b) as the backbone. For sequential editing with a trainable editor, we also use an Audit baseline trained single-step audio editing. In addition, we evaluate the same set of baseline methods on single-step audio editing tasks. More details available in Appx. B.I
Implementation details. SmartDJ ALM is initialized from AF2 (Ghosh et al., 2025a) with 3B parameters. During training, we freeze the AF-CLAP module and fine-tune the adapter layers and LLM with LoRA module for 20 epochs with batch size of 24. For LDM, it uses velocity prediction and CFG rescaling technique (Lin et al.) 2024) to adjust the magnitude of the predicted velocity and avoid over-exposure. It trained on single-step editing data with batch size of 256 of 500k iterations. 10% text is replaced with empty strings to enable unconditional modeling. The learning rates for the ALM and LDM training are le-5 and Se-5 with AdamW. All experiments are conducted with four NVIDIA L40S GPUs. More details available in Appx. B.2
4.2 RESULTS
Declarative instruction audio editing. We first show inference examples from our ALM module in Fig. The ALM- ALM-generated atomic editing steps accurately align both the original audio contents and the declarative editing instructions. For example, it correctly removes audio event engine roar when transferring audio scene into a busy family home vibe. It also removes people whistle and adds pages turning to enhance the immersion of being in an old library. More examples in Appx. C.T
We present the results of the declarative instruction editing task in Tab. T The first row is the end-to-end Audit baseline, which iss directly trained on declarative instructions and the final target audio, showing the worst performance overall. Since no prior method can interpret the declarative instructions, we use the same set of A.L--generated atomic steps to guide all audio editing models in the multi-step evaluation, including the baselines and our method. We compare the edited audios from each method with 1k reference audios. Our method achieves the lowest metric in FD, FAD, LSD, and comparably low in KI., indicating the smallest discrepancy from the reference audios. It
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Framework
Method
Training
Speed
FD 4
FAD 4
KLL
LSD4
CLAPT
w/o ALM
Audit
2.07s
28.56
10.00
3.07
1.93
0.11
SDEdit
301s (74.6s)
19.66
3.71
3.25
2.22
0.17
DDIM
331s 18).1s)
24.70
9.43
4.06
2.20
0.07
ZETA
356s (88.2s)
20.74
3.73
2.92
2.21
0.20
w/ ALM
AE
X
406s (101s)
19.91
4.99
3.21
2.08
0.19
Audit
11.6s (2.07
21.50
5.67
2.80
1.49
0.18
Sm (Ours)
13.1s (2.40s)
10.60
1.52
2.84
1.40
0.21
Table 1: Quantitative results the whole pipeline from declarative instructions to audio editing. Speed in (-) is the time for single-step edit. AE denotes AudioEditor; DDIM denotes DDIM Inversion.
Extract whistling. speaking and laughing
Change
up
birds chirping the front
speaks and applauds
applausa and by
Soith dredon
Removed
Original
Soand daciin
Prspurcc
Extracted sound
Enhanced
Pred
Sound direction changed
Stund dieciion
Target
Figure 6: Examples on Remove, Extract, Change, Turn up operations. SmartDJ edited audio are closely aligned with the ground truth. Yellow boxes highlight edited sound components.
also delivers high IS metric, showing strong audio quality and diversity. In addition, the highest CLAP score demonstrates the best semantic alignment between the edited audio and instruction.
Inference time. We also provide inference speed analysis in Tab. The inference speed reported outside the brackets denote the total time to complete the entire declarative instruction edit, while the values inside indicate the per-round inference time of LDM. On average, SmartDJ's ALM requires 4.8s to generate one set of atomic editing instructions. With multi-round reasoning, SmartDJ completes a full edit 1s, which significantly faster than training-free baselines. Our approach is slower compared with end-to-end Audit (2.07s) Audit with ALM (1 (11.6s), but this trade-off yields substantially better editing quality and alignment with target instructions.
Single-step audio editing. We present the results of single-step audio editing operations. Tab. 2a shows results on add. Our method consistently outperforms baseline methods in the edited audio, including better similarities the ideal target audio (lowest FD, FAD and KL), and higher quality and diversity shown by the highest IS. Furthermore, stronger spatial metrics also indicate that the stereo audio characteristics are preserved better by SmartDJ.
Tab. also shows the performance on Remove and Ext ract tasks. The results clearly indicate that our method delivers the best performance in aligning with the ground truth edited audio across both operations. We show the results on Vol ume, Time, Reverberat ion, Timbre and Change sound direction tasks in Tab, 26 and 2c Our method again shows stronger performance over Audit and training-fre free baselines, which demonstrates SmartDJ has better fine-gre lie-ggrined manipulation in audio event properties. This is because Audit's VAE operates on mee-strctssgrams and discards phase information, which is critical for spatial cues. SmarDDJ employs a diffusion transformer, which provides stronger long-range temporal modeling and richer cross-attention conditioning. These architectural upgrades allow edits to be both semantically precise and spatially coherent. Some examples of spectrogram visualization are shown in Fig. 6 More qualitative comparisons can be found in Appx.
Human evaluations. We conducted a blind, pairwise A/B preference survey with 19 participants.
Each participant evaluated 20 randomly sampled pairs (10 declarative editing, 10 single-step editing). Each data pair consists the original audio, the editing prompt, SmartDJ edited result, and edited results from random competing baseline. In each pairwise comparison, the participants were asked
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Add
Remove/ / raxt
Method
FD
FAD 4
:
GCC
FSAD
FAD]
KL1
LSD
GCC)
CRWL
FSAD.
SDEdit
25.79
4.46
2.57
2.10
73.15
209.67
0.28
36.32
4.13
2.20
1.88
52.31
102.48
0.28
DDIM
28.84
7.14
2.11
185
0.07
42.01
5.06
2.54
52.35
ZETA
29.38
4.14
2.44
1.79
73.15
203.60
0.35
31.64
3.36
1.77
1.98
$3.06
101.18
0.27
AE
23.84
4.16
2.11
1.92
68.15
232.64
0.57
36.71
3.25
2.24
2.00
53.79
107.48
0.36
Audit
27.82
74.37
217.49
0.21
6.73
1,96
132.72
SmanDD
17.74
2.07
1.38
1.41
39.05
65.90
0.02
20.27
2.29
0.95
1.70
5.22
16.55
0.01
(a) Operation add and wewove/eeteeeee
Operation
Method
FAD]
KL4
LSD]
GCCL
CRW
FSAD
Volume
SmarDD
Audit
36.56
9.19
4.56
0.82
0.24
1.33
1.04
1.01
83.71
1.75
121.10
20.30
0.53
0.01
Method
PDJ
FAD]
Change Bound Dizection
KL.
LSDL
GCC|
CRW]
FSAD]
Time
Smart
Audit
33.76
10.59
4.15
1,02
0.45
1.52
1.12
1.31
167.25
2.38
81.%6
16.90
0.70
0.08
SDE
DDIM
41.52
29.69
57.56
2.85
2.19
1.37
1.20
1.31
82.09
70.18
212.78
211.98
0.09
0.25
Reveut
SmartD
Audit
39.
10.83
4.36
0.59
1.04
ZETA
Audit
30.43
1,42
1.03
0.94
67.29
72.17
205.1 18
0.51
Timboe
SimanD
Audit
27.11
8,80
0.71
2.67
0.35
1.02
1.10
1.05
00.21
3.04
164.00
29.90
0.61
0.09
Smart)
9.45
0.67
0.27
1,02
26.02
48.69
0.01
(c) Operation change sound direction
(b) Operation Vol umc, T Time, Reverb and Timbre
Table 2: Quantitative results all individual audio editing operations.
Complex edit quality
Complex edit alignment
Log Spec. Distance
SmartD)
90.41% %
Audit
SmartD)
93.15%
Audit
0.6
SmarlD)
95.52%
AE
SmartD)
91.04%
AE
AudioEdltor
ZETA
SmartD)
80. 80.00%
ZETA
SmartD)
97%00%
ZETA
Audit
0.4
Single step quality
Single step alignment
SmartD]
SmartD)
95.35%
Audit
SmartD)
76%76%
Audit
Smartb)
27%
AE
SmartD)
92%
0.2
SmartD)
77.36%
ZETA
SmartD)
88.89%
ZZETA
Number of edit rounds
Figure 7: User study results. In both audio quality and text/audio alignment, SmartDJ is consistently preferred over baselines in the declarative instruction editing task and single-step tasks.
Figure 8: Similarity with origi- nal audio after multiple editing rounds.
to select one of the edited audio with higher quality, better alignment to the text or spatial instructions, and better alignment to the original audio. We separate the evaluation for declarative instruction audio editing and single-step editing in Fig. 7 In both tasks, SmartDJ is much preferred over all competing methods with higher win-rate. Our method delivers the best audio quality and the best alignment with both declarative instructions and single-step instructions. We also use a two-sided Z-test and find SmartDJ demonstrates statistical significance (p < 0.001). We find that our 95% confidence intervals further confirm robustness, with all lower bounds (ranging from 66. 1% to 94.2%) exceeding the random chance threshold of 50%. More details of the user study are listed in Appx. C.4
4.3 ABLATION STUDIES
Performance of ALM generate editing steps. We use BERTScore (Zhang al. 2019) to evaluate the ALM generated results. It measures semantic similarity between two sentences with a pretrained BERT model. For our task, each detailed editing plan consists of multiple atomic steps. To ensure fair evaluation, we group atomic operations by type (add, remove and so on), and compare only between matching operation types in the GT and generated plan. We then compute BERTScore for each operation type and report the average across all operation groups (results in 91.8% Precision, 92.0% Recall and 91.5% of FI). We also provide some failure case amalysis in Appx. where the ALM is not able to fully understand contradictory instructions.
Audio quality over multi-round editing. Since the declarative instruction audio editing task involves sequence of editing, unchanged content must remain intact after multiple steps. To evaluate this, we design a "round-trip" edit experiment: we perform the operations "add the sound AA" and "remove audio clip for five rounds, where A is pseudo audio label. For ideal audio editor, this sequence of round-trip operation should exactly reconstruct the original audio. We measure the LSD between each round's 's edited output with the original audio clip (Fig, [8). SmartDJ consistently achieves the lowest LSD, indicating the smallest drift from the original content. This demonstrates that our method preserves the unedited audio content under repeated editing actions.
the sound of A"
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Published as a conference paper at ICLR 2026
Study Objectives
Variation
FD4
FAD
KLL
LSD
CLAP
w/o ALM Apption-GPT-4o+mmaaatt editor w/ SmartDJ ALM
23.6
3.14
2.91
1.84
0.137
16.8
2.70
2.96
1.45
0,184
ALM module
14.7
1.53
2.85
1,42
0.238
Add - Modifg fy + Remove
14.9
1.59
2,88
1.48
0,234
Editing order
Random order
14.7
1.56
2.88
1.47
0.237
Remove + Modify + Add
14.7
1.53
2.85
1.42
0.238
AudioSep (Liu al. 2024b
27.1
2.86
0.88
1.63
N/A
Ext pact operation
SmarlD]
25.7
2.55
0.78
1.71
N/A
Table Model ablations.
Effectiveness of ALM. We conduct an ablation by removing the ALM module and training a variant of the LDM end-to-end. As shown in Tab. 3 SmartDJ performs significantly worse without ALM, showing the importance of ALM's intermediate reasoning capabilities. ALM enables the model to produce semantically coherent edits aligned with the declarative instruction and the original audio.
Choice of ALM. To assess the sensitivity of SmartDJ to the choice of ALM backbone, we replaced our AF2-based ALM with LTU (Gong et al., (023) while keeping the editor unchanged. As shown in Tab. 3 using LTU results in slightly worse performance. We attribute this gap primarily to the fact that our ALM is pretrained on a much larger and more diverse corpus, giving stronger performance on decomposing instructions. Nevertheless, the LTU+SS editor variant still produces reasonable results. This indicates that our framework is generalizable to different model ALM architectures.
Can Caption Model LLM replace ALM? We evaluate captioning-basee alternative pipeline, using a DCASE captioning model (Xul 2024) to describe the audio and GPT-4o to produce step-wise instructions executed by SmartDJ editor. As shown in Tab. 3 this sattioning-based pipeline performs consistently worse than SmartDJ. We attribute the performance drop to two factors: (1) Captioning models summarize only dominant events and often miss fine-grained overlapping sounds. (2) Missing or misidentified events in the caption propagate through the L.M's reasoning, compounding into hallucinated or non-exccutable editing instructions with cumulative error.. But future works can explore using strong audio captioning model to directly get the audio events and prompt LLM to achieve the declarative editing.
Editing order. We adopt a simple ordering strategy of remove - modify (volume/direction) + add, which intuitively avoids removing newly inserted content. To the impact of ordering, we also experiment with two alternatives: randomized and reversed order. As shown in Tab. 3 both alternatives result in only marginal degradation compared to our default ordering. This indicates that the editing order has minimal influence on the final results, suggesting that ALM-generated instructions rarely contain conflicting operations.
Compare with sound separation model. We compare SmartDJ with recent sound separation model AudioSep (Liu et al. 2024bb on the Ext ract operation. Ours achieves comparable or slightly better performance (Tab. 3 This suggests that while model is designed for general- purppoe audio editing, it is competitive on target sound separation tasks.
5 DISCUSSION
This work takes an initial step toward declarative stereo audio editing by bridging high-level instruc- tion understanding with low-level generative transformation. Rather than treating audio editing as monolithic operation, our framework decomposes complex user intent into atomic and sequentially executable steps. While extending the generative audio editor to support entirely new task-specific operations currently requires retraining, many realistic edits can already be composed from a small set of reusable atomic operations. Looking forward, promising avenue is to unify reasoning and generation through end-to-end joint training of audio language models and generative backbones, enabling tighter coupling between semantic intent and low level signal editing. More broadly, believe this paradigm opens new opportunities for interactive audio creation systems and pushes toward more general general-purpose audio intelligence.
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6 ETHICS STATEMENT
Our work focuses on audio editing for research and creative applications such as immersive media, conferencing, and sound design. The dataset is generated from publicly available sound event libraries and synthetic mixing, without personal or sensitive recordings. We encourage responsible use aligned with academic and creative purposes.
REPRODUCIBILITY STATEMENT
We provide detailed descriptions of the model architectures, training objectives, and data generation pipeline in the main paper and appendix. Hyperparameters, training configurations, and dataset con- struction details are included to ensure reproducibility. Code, pretrained models, and the synthesized dataset will be released upon acceptance facilitate replication of our results.
ACKNOWLEDGGENNS
We thank the members of the WAVES Lab at the University of Pennsylvania for their valuable feedback. We are grateful to the anonymous reviewers for their insightful comments and suggestions.
REFERENCES
Tim Brooks, Aleksander Holynski, and Alexei A. Efros. Instructpix2pix: Learning follow image editing instructions,
Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio- visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 721-725. IEEE, 2020.
Ziyang Chen, David F Fouhey, and Andrew Owens. Sound localization by self-supervised time delay estimation. In European Conference on Computer Vision, pp. 489-508. Springer, 2022.
Ziyang Chen, Prem Seetharaman, Bryan Russell, Oriol Nieto, David Bourgin, Andrew Owens, and Justin Salamon. Video-guidee guided foley sound generation with multimodal controls. arXiv preprint arXiv:2411.17698, 2024.
Tianheng Cheng, Lin Song, Yixiao Ge, Wenyu Liu, Xinggang Wang, and Ying Shan. Yolo-world: Real-time open-vocabulare object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16901-16911, June 2024.
Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shiliang Zhang, Zhijie Yan, Chang Zhou, and Jingren Zhou. Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models. arXiv preprint arXiv:2311.07919 2023.
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et Scaling instruction-fietuueed language models. Journal of Machine Learning Research, 25(70):1-53, 2024.
Guillaume Couairon, Jakob Verbeck, Holger Schwenk, and Matthieu Cord. Diffedit: Diffusion- based semantic image editing with mask guidance, 2022. URL
11427
Danny Driess et. al. Palm-e: An embodied multimodal language model, 2023. URL https
f/arxiv.orgg www.xiwworg/ab///33..o
Jean-Baptiste Alayrac et al. Flamingo: a visual language model for few-shot learning, 2022. URL
https://arxv.orr//aap
Zach Evans, CJ Carr, Josiah Taylor, Scott Hawley, and Jordi Pons. Fast Liming-conditinn latent audio diffusion. In Forty-first International Conference Machine Learning, 2024a.
11

---

Published as a conference paper at ICLR 2026
Zach Evans, Julian D Parker, CJ Carr, Zack Zukowski, Josiah Taylor, and Jordi Pons. Stable audio open. arXiv preprint arXiv:2407.14432 2024b.
Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, and Xavier Serra. Fsd50k: an open dataset of human-labeled sound events. IEEE/ACM Transactions Audio, Speech, and Language Processing, 30:829-852, 2021.
Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan. Guiding instruction-based image editing via multimodal large language models, 2024. URL https
//arxiv.org 2209. 17102
Zhiqi Ge. Hongzhe Huang, Mingze Zhou, Juncheng Li, Guoming Wang, Siliang Tang, and Yueting Zhuang. WorldGPT: Empowering L.MM as multimodal world model. In ACM Multimedia 2024, 2024. /
net/ forum? d=G1 t.garrrwww
Zigang Geng, Binxin Yang. Tiankai Hang. Chen Li, Shuyang Gu, Ting Zhang, Jianmin Bao, Zheng Zhang. Han Hu, Dong Chen, and Baining Guo. Instructdiffusion: generalist modeling interface for vision tasks, 2023. URL http://awww..cra/////aaaaaaaaa/a
03895
Sreyan Ghosh, Zhifeng Kong, Sonal Kumar, S Sakshi, Jachyeon Kim, Wei Ping, Rafael Valle, Dinesh Manocha, and Bryan Catanzaro. Audio flamingo 2: An ludio-language model with long-audio understanding and expert reasoning abilities. arXiv preprint arXiv:2503.03983 2025a.
Sreyan Ghosh, Zhifeng Kong. Sonal Kumar, S Sakshi, Jachyeon Kim, Wei Ping, Rafael Valle, Dinesh Manocha, and Bryan Catanzaro. Audio flamingo 2: An audio-language model with long-audio understanding and expert reasoning abilities, 2025b. URL htttt:://taaiiiii
abs /2503
03983
Yuan Gong, Hongyin Luo, Alexander H Liu, Leonid Karlinsky, and James Glass. Listen, think, and understand. arXiv preprint 13X1:22305.10790 2023.
Jiarui Hai, Yong Xu, Hao Zhang, Chenxing Li, Helin Wang, Mounya Elhilali, and Dong Yu. Eza- udio: Enhancing text-to-audio generation with efficient diffusion transformer. arXiv preprint arXiv:2409. 2024.
Yiduo Hao, Sohrab Madani, Junfeng Guan, Mohammed Alloulah, Saurabh Gupta, and Haitham Hassanich. Bootstrapping autonomous driving radars with self-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15012- 15023, June 2024.
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt- to-prompt image editing with cross attention control, 2022.
Mojtaba Heydari, Mehrez Souden, Bruno Conejo, and Joshua Atkins. Immersediffusion: generative spatial audio latent diffusion model. arXiv preprint arXiv:2410. 14945, 2024.
Mojtaba Heydari, Mehrez Souden, Bruno Conejo, and Joshua Atkins. Immersediifusiin: generative spatial audio latent diffusion model. In ICASSP 2025-2025 IEEE International Conference on Acousties, Speech and Signal Processing (ICASSP). pp. 1-5. IEEE, 2025.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 2022.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang. Lu Wang, Weizhu Chen, al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
Jiawei Huang, Yi Ren, Rongjie Huang, Dongehao Yang, Zhenhui Ye, Chen Zhang, Jinglin Liu, Xiang Yin, Zejun Ma, and Zhou Zhao. Make-an-audio 2: TTmpooal-enhanced text-to-audio generation. arXiv preprint arXiv:2305. 184744 2023a.
Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong. Rui Huang, Ruimao Zhang, and Ying Shan. Smartedit: Exploring complex instruction based image editing with multimodal large language models, 2023b. URL https
7/ rxxiv, wwwaaiivorrgaaa wttp:oo/g/a/aa//eoo 06739
12

---

Published as a conference paper at ICLR 2026
Mude Hui, Siwei Yang, Bingchen Zhao, Yichun Shi, Heng Wang, Peng Wang. Yuyin Zhou, and Cihang Xie. Hq-edit: A qugl-quality dataset for instruction-bart based image editing. arXiv preprint MrXiv:2404.09990, 2024.
Yuhang Jia, Yang Chen, Jinghua Zhao, Shiwan Zhao, Wenjia Zeng, Yong Chen, and Yong Qin. Audioeditor: A training-free diffusion-based audio editing framework. arXiv preprint arXiv:2409. /2466, 2024.
Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, and Gunhee Kim. Audiocaps: Generating captions for audios in the wild. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume (Long and Short Papers), pp. 119-132, 2019.
Jaeyeon Kim, Heeseung Yun, and Gunhee Kim. Visage: Video-to-spatial audio generation. arXiv preprint wrX.v:2556.1219999 2025.
Jing Yu Koh, Daniel Fried, and Ruslan Salakhutdinov. Generating images with multimodal language models, 2023. URL https: arxiv. wwwplvv.rrraaaaaaoe ablp//5/b//3335.a.a 17216
Zhifeng Kong, Arushi Goel, Rohan Badlani, Wei Ping, Rafael Valle, and Bryan Catanzaro. Audio flamingo: A novel audio language model with few-shot learning and dialogue abilities. arXiv preprint 1xXi::2402.08811, 2024a.
Zhifeng Kong, Arushi Goel, Rohan Badlani, Wei Ping, Rafael Valle, and Bryan Catanzaro. Audio flamingo: A novel audio language model with few-shot learning and dialogue abilities, 2024b.
URL httphhttp///xiiii http:///wxxxvv.g.//aa/// http://bb/2442/ 01831
Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, and Kundan Kumar. High- fidelity audio compression with improved rvqgan. Advances in Neural Information Processing Systems, 36:27980-27999 2023.
Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia. Lisa: Reasoning segmentation via large language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pp. 9579-9589, June 2024.
Zitong Lan, Chenhao Zheng, Zhiwei Zheng, and Mingmin Zhao. Acoustic volume rendering for neural impulse response fields. arXiv preprint arXiv:2411.06307, 2024.
Zitong Lan, Yiduo Hao, and Mingmin Zhao. Resounding acoustic fields with reciprocity. arXiv preprint arXiv:2510.20602, 2025a.
Zitong Lan, Yiwei Tang, Yuhan Wang, Haowen Lai, Yiduo Hao, and Mingmin Zhao. Building aisiol digital twins with smartphones. arXiv preprint vxXiv25512.1077 2025b.
Chunyuan Li, Zhe Gan, Zhengyuan Yang, Jianwei Yang, Linjie Li, Lijuan Wang. and Jianfeng Gao. Multimodal foundation models: From specialists guueros-purpose assistants, 2023a. URL
hittps://aras
org/ abs 2309.9.. 10199.0..0 10020
Xiaogi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong. Manipllm: Embodied multimodal large language model for object-centric robotic manipulation, 2023b.
6617
Jinhua Liang, Huan Zhang, Haohe Liu, Yin Cao, Qiuqiang Kong, Xubo Liu, Wenwu Wang, Mark Plumbley, Huy Phan, and Emmanouil Benetos. Waveraft: Audio editing and generation with large language models. arXiv preprint arXiv:2403.09527 2024.
Shanchuan Lin, Bingchen Liu, Jiashi Li, and Xiao Yang. Common diffusion noise schedules and sample steps are flawed. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 5404-5411, 2024.
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley. Audioldm: Text-to-audic generation with latent diffusion models. arXiv preprint arXiv:2301.12500 2023a.
13

---

Published as a conference paper at ICLR 2026
Haohe Liu, Yi Yuan, Xubo Liu, Xinhao Mei, Qiuqiang Kong, Qiao Tian, Yuping Wang, Wenwu Wang, Yuxuan Wang, and Mark D Plumbley. Audioldm 2: Learning holistic audio generation with self-supervised pretraining. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024a.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. 2023b. URL https://arxiv. www.////////oooooooa//..a/aa.a.a
08485
Huadai Liu, Tianyi Luo, Qikai Jiang, Kaicheng Luo, Peiwen Sun, Jialei Wan, Rongjie Huang, Qian Chen, Wen Wang, Xiangtai Li, et al. Omniaudio: Generating spatial audio from 360-degree video. arXiv preprint arXiv:2504. 115500444400 J4906, 2025.
Xubo Liu, Qiuqiang Kong, Yan Zhao, Haohe Liu, Yi Yuan, Yuzhuo Liu, Rui Xia, Yuxuan Wang, Mark D Plumbley, and Wenwu Wang. Separate anything you describe. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024b.
Xinji Mai, Zeng Tao, Junxiong Lin, Haoran Wang, Yang Chang, Yanlan Kang, Yan Wang, and Wengiang Zhang. From efficient multimodal models to world models: A survey, 2024. URL
pettps:/aarxor org/ abs 2407.00118
Hila Manor and Tomer Michaeli. Zero-shot unsupervised and text-based audio editing using ddpm inversion. arXiv preprini arXiv:2402.10009, 2024.
Xinhao Mei, Chutong Meng, Haohe Liu, Qiuqiang Kong, Tom Ko, Chengqi Zhao, Mark D Plumbley, Yuexian Zou, and Wenwu Wang. Wavcaps: A chatgpt-assisted weakly-labelled audio captioning dataset for audio-language multimodal research. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024.
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022. URL
https::/arxiv. org/ abs /bs/2 01073
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models, 2022. URL https::/arxxiv.org//
2211.09794
Yao Mu, Qinglong Zhang. Mengkang Hu, Wenhai Wang, Mingyu Ding, Jun Jin, Bin Wang, Jifeng Dai, Yu Qiao, and Ping Luo. EmbodiedGPT: Vision-language pre-training via embodied chain of thought. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL
wet.iiewweeeeeeeeeeeee.eee.eeeee net dtte/foorumm11-11520000
Karol J Piczak. Esc: Dataset for environmental sound classification. In Proceedings of the 23rd ACM international conference on Multimedia, pp. 1015-1018, 2015.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and puttern recognition, pp. 10684-10695, 2022.
Robin Scheibler, Eric Bezzam, and Ivan Dokmanić. Pyroomacoustics: python package for audio room simulation and array processing algorithms. In 2018 IEEE international conference acoustics, speech and signal processing (ICASSP), pp. 351-355. IEEE, 2018
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Inter- national Conference on Learning Representations, 2021.
net/
Peiwen Sun, Sitong Cheng, Xiangtai Li, Zhen Ye, Huadai Liu, Honggang Zhang, Wei Xue, and Yike Guo. Both cars wide open: Towards language- driven spatial audio generation. arXiv preprint arXiv:2410.10676 2024.
Andrew Szot, Bogdan Mazoure, Harsh Agrawal, R Devon Hjelm, Zsolt Kira, and Alexander Toshev. Grounding multimodal large language models in actions. In The Thirry- Annual Conference on Neural Information Processing Systems, 2024. URLpattps://openeeee 5WxY 6es
14

---

Published as a conference paper at ICLR 2026
Qwen Team. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923, 2025.
Yuancheng Wang, Zeqian Ju, Xu Tan, Lei He, Zhizheng Wu, Jiang Bian, al. Audit: Audio editing by following instructions with latent diffusion models. Advances in Neural Information Processing Systems, 36:71340-71357, 2023.
Jialong Wu, Shaofeng Yin, Ningya Feng, Xu He, Dong Li, Jianye HAO, and Mingsheng Long. ivideoGPT: Interactive videoGPT's are scalable world models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024a. URL BtLLpttts///peeeee net fost/forum?idee TENzBft2R
Jiannan Wu, Muyan Zhong, Sen Xing, Zeqiang Lai, Zhaoyang Liu, Zhe Chen, Wenhai Wang, Xizhou Zhu, Lewei Lu, Tong Lu, Ping Luo, Yu Qiao, and Jifeng Dai. VisionLLM v2: An end- to-end generalist multimodal large language model for hundreds of vision-language tasks. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024b. URL net
Jianzong Wu, Xiangtai Li, Chenyang Si, Shangchen Zhou, Jingkang Yang, Jiangning Zhang, Yining Li, Kai Chen, Yunhai Tong, Ziwei Liu, and Chen Change Loy. Towards language-driven video inpainting via multimodal large language models, 2024c. URL
2401 00266
Qingxuan Wu, Zhiyang Dou, Chuan Guo, Yiming Huang, Qiao Feng, Bing Zhou, Jian Wang, and Lingjie Liu. Text2interact: High-fidelity and diverse text-to-twar tto-two-peroo interaction generation, 2025. URL httttttpp//axtttt
org abs 2550006604
Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg- Kerg-Kirkpatrick, and Shlomo Dubnov, Large-scale contrastive language-audie pretraining with feature fusion and keyword-to-captin augmentation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5. IEEE, 2023.
Bin Xia, Shiyin Wang, Yingfan Tao, Yitong Wang, and Jiaya Jia. Llmga: Multimodal large language model based generation assistant. arXiv preprint arXiv:2311.16500, 2023.
Manjie Xu, Chenxing Li, Dan Su, Wei Liang, Dong Yu, et al. Prompt-guided precise audio editing with diffusion models. arXiv preprint arX1v:2406.04350, 2024.
Xuenan Xu. Audiocaption. http:////ttttaoccc/////aaaaaaaaa 2024.
GitHub repository, accessed 2025-11-21.
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert. arXiv preprint 1rXiv:1904.0967,,000 2019.
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A DATASET CURATION PROCESS
A.1 DATASET PREPARATION
We construct our training corpus by merging several publicly-availabl audio dataset, including VGG- Sound, AudioCaps, WavCaps, ESC-50, and FSDS0K. Since some of these sets provide audio captions rather than discrete audio label, first convert every caption to audio labels with GPT-4o-minn -inii API. We only retain single-labeled audio clip and any clip whose caption maps to multiple events is discarded. A CLAP model scores the semantic correspondence between the audio and its new label. Samples with similarity score below 0.3 are filtered out. The remaining clips from all sources are finally mixed into one large dataset that we use for subsequent data curation.
A.2 DETAILED IMPLEMENTATION OF SPATIAL AUDIO
We simulate room impulse responses using PyRoomAcoustin (Scheibler et al. 2018 to generate stereo audio in diverse environments. Rectangular rooms are created with horizontal dimensions sampled from
R= Ro. R1, Rgl.
Ro. R1 ~ (4, 10) m,
R2 3
(6)
All microphone and source positions sampled uniformly inside the room with minimum 0.2m to each wall, and the microphone azimuth orientation is drawn at random.
The reverberation time of RIR is sampled as RT60 (0.3, 1.2) covering different level of reverberation conditions.
Directional sampling. We consider semantic source directions (left, front-left, front, front-right right) and map each label to an azimuth angle E {180", 135%, 90%, 45% _0 To introduce variability, we perturb the azimuth with Gaussian noise (standard derivation of 5"). This 0 represents the relative angle between the microphone orientation and the sound source.
Binaural rendering with cardioid gain. We use two-microphone array with spacing d = 17cm to simulate human ears. Let ztt) denotes the dry source signal, and let be the incident angles of the source relative to the left or right microphone normals. We use cardioid gain pattern G(a)) = 1/2 (1 + cos ar). The rendered left and right ear signals are
(t xxt) hz((), (R(() (() * hx((),
(7)
(8)
where hz(t) (t) and hx(() are the RIR synthesized at the left and right position, denotes convolution. The synthesized RIR considers the directional dependent gain pattern when combining the acoustic ray coming from different directions. As result, the interaural time and level difference are encoded within the synthesized RIR to create spatial audio experience.
A.3 ATOMIC EDIT ACTIONS
We explain the details on creating the single-step atomic edit data pairs. For this single-step audio editing, we have an original audio a;-1, single atomic edit operation s,. Base on atomic edit operation 8,, we can generate the edited audio a,.
Add. Assume the original audio is mix of audio content A+ B+C. To add a new content into this original audio, we sample new audio content from the datahase and mix the D with the original audio A ++C+ The atomic template is "Add the sound of (dog barking/ at (right) with (3) db".. The contents inside the { can be changed to other sound events or sound attributions. We support various sound direction (left, front and right) and dynamic volume adjustment.
Remove. Given an original mix B+, let B be the undesired source. We suppress B so the output becomes A+C. The atomic template is Atomic template: "Remove the sound of (bird chirping/ fat right)". " The directional phrase in braces is optional. If there are similar audio contents in the same clip, the spatial features enables manipulate it precisely.
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Extract. Starting from the same mix A+ +CC, isolate target source A and mute everything else, yielding only A. The atomic template is "Extract the sound of (speaking] (at the right/". The direction is optional.
Turn up/down To change loudness of a specific source B, we scale it by a, where a > is for "up" and 0< <1 is for "down". The resulted audio clip is A aB + C. The atomic template is "Turn down) the sound of (engine rev) by (2) dB". We also support a dynamic range of volume
(up / adjustment.
Change sound direction. We alter only the spatial cues of a specific source C, producing an edited version C' while leaving the other tracks untouched: A+B+C'. The atomic template used is "Change the sound of (baby crying] [from front] to (right)". . The "from" clause could also be omitted.
Time shift. Given an original mix A+ B+CC let B be the target source. We apply time shifting B so the output becomes A+ B' +C. The atomic template is Atomic template: "Shift time of the sound of (bird chirping) (by 3 seconds)".
Add reverberations. Given mix A+B+C, we apply reverberation to specific target source (e.g., B) by convolving it with a room impulse response (RIR). The resulting edited clip becomes A + B' C. B' = B * ReReevl-- We support three reverberation levels, each corresponding to an RIR with a different decay time (RT60):
Low reverberation: RT60 = 0.4
Mid reverberation: RT60 0.8
High reverberation: RT60 1.2s
The atomic template is "Add reverberation to the sound of [violin) [al lest) of (low / /id/ / high) level". A directional qualifier (e.g., "at left") optional. The reverberation is applied only to the selected source, while all other sources remain unchanged.
Timbre adjustment. We modify the spectral characteristics of a specific source C while preserving its spatial cues and temporal structure. The resulting signal C" is obtained via frequency-domann filtering, and the output becomes B+++C'. We support several interpretable timbre transformations
commonly used in audio production:
Bright: boost high frequencies kHZ
Dark: attenuate high frequencies kHZ
Warm: boost mid-high components (300 Hz-4kHZ)
Cold: boost > 6 kHz and attenuate < 300 Hz
Muffled: attenuate frequencies kHz
The atomic template is "Change the timbre of the sound of (acoustic guitar] to (bright / dark / warm / cold / muffled)". ". These adjustments operate at the source level, allowing precise sound-color manipulation even when multiple acoustically similar sources appear within the same audio clip.
A.4 COMPLEX AUDIO EDITING DATASET CURATION
In the dataset curation process, first sample 2-4 audio labels in the database, with LLM randomly assigned volume and sound directions. We then call the GPT-4o batch API with sound sources and attributions. For each API call, we provide 15 sets of sound sources. Through API call, each set of sound sources will return single data pair containing Declarative editing instruction and the corresponding atomic editing steps. We follow these atomic editing steps manually generate the step-by-step step target edited audio with the rules in A.3 We generate 240K data pairs for complex audio editing for training. We generate 2K data pairs from AudioCaps rest/validation set for evaluation.
For the single step audio editing pairs (8;, 0:-1, 02), we further scale up the this dataset size to 1M to train the LDM audio editor and we also generate another 2K extra audio data pairs to evaluate the performance of single step audio editing. This scaled-up single step audio editing dataset keeps some instructions with audio captions, improving the audio editor's 's robustness to different audio contents in the atomic edit instructions. Tab. 4 shows comparison between current audio generation and editing dataset, where ImmerseDiffusion (Heydari et al., 2025) and YT-Ambigen (Kim et al. 2025 current spatial audio generation work.
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Dataset
Task
Hours
Number
Data Type
Format
Audio Generation Datasets
ImmerseDDiffssinn YT-Ambigen BEWO- IM (text)
Text Audio
6000
1.M
Text Audio
FOA
Video to Audio
102K
Video + Audio
FOA
Text to Audio
2800
1M
Text + Audio
Binaural
BEWO-IM 1M (image)
Image to Audio
54
2.3K
Image + Audio
Binaural
Audio Editing Datasets
Audit
1605
578K
Text + Audio
Mono
Add
197
71k
Remove
197
71k
Replacement
139
50k
Inpainting
536
193k
Super-resolution
536
193k
SmartDJ
Single-Step Editing
2778
IM
Text Audio
Binaural
Add
768
277K
Remove
255
92K
Extract
256
92K
Volume adjust
510
183K
Direction adjust
255
92K
Timing adjust
245
88k
Add Reverb.
245
88K
Timbre adjust
244
88k
SmartDJ Declarative Editing
Declarative editing
667
240K
Text Audio
Binaural
Table 4: Summary of current audio generation and editing datasets.
We provide the details of our Base Prompt for dataset curation as follow:
Prompt
You are an expert in spatial audio editing and sound design.
Your task generate complex audio editing instructions based on a given list of sound sources (labels). The sound sources will provided as list full sentences (as strings), not character lists. Treat each sentence as a single atomic sound unit. Do not tokenize or split the sound sources into characters.
For example, if you are given: "a baby crying and should consider "a baby crying a man talking" as complete sound source. "a bird is chirping" is another complete sound source and "dog barking" is another sound source. You then generate the step-by-step audio editing instructions based on the given complex instructions.
talking; bird is chirping: dog barking". You
Task: you to brainstorm a complex audio editing instruction
Imagine a realistic and creative soundscape editing for the given audios.
You are not limited to the provided audio contents for the editing instruction. And the complex editing instructions should be brief.
The complex editing instructions could be soundscape transformation. For example:
Make this sound it was recorded bookstore
Make this sound like busy coffee shop
Make this sound like a train station
Make this sound like forest night
Make this sound like a beach
Make this sound like sunny day
Craft this sound to feel like park
Make this audio sound like quiet farm
Make this audio sound like firework show
Your generated complex instructions be broader than these provided examples. Use your imaginations!
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But remember keep them brief, the complex editing instructions ideally should not contain the actual sound sources.
Then, based on the given sound event, give me detailed editing instructions.
You then generate detailed editing instructions based on the complex audio editing instruction.
Ensure the editing makes sense (e.g.. no waves in desert, no sheep indoors, rustling leaves in the forest, seas waves in the beach, raining in the sunny day).
You are encouraged to remove the original audio contents, but you required maintain at least one sound source, removing all of them.
Do not split or partially reference sound source when applying operations.
Use a combination of simple operations (but NOT necessarily all them), For example:
Add (e.g., thunderstorm, meowing) (for the add operation, you should add up to two additional sources that best align with complex editing instructions)
Remove (For remove, should remove at least one sound sources (up to two sound sources), but
you must keep at least sound source!)
Turn up/down (e.g.. turn up/turn down the sound of xxx by xxx db (between 0 and 6db))
Change sound direction.
Add reveberations (low, medium, or high level)
Change timbre (choose from bright, dark, warm, cold, muffled)
Each "target" remove/turn up/tum down/change must exactly match one the provided "sound sources"
For the "add" operation:
TTe "target" must clearly describe new sound heing added (e.g.. "crowd chatter", "rain",
"footsteps on gravel").
"effect" should specify volume direction (e.g., "at front by 4dB").
Do NOT use "none". "null", placeholder values the target. The target must always be descriptive label the added sound.
The added sound should not duplicate any original sound source.
The same operation can repeated for multiple targets.
When doing add, you can also have volume and direction attributes
When editing existing sound sources, you can also have mixed attributes in terms of the volume, sound directions and timbre, also adding reverberations.
But limit the total number of detailed editing steps between
You must ensure each step logically contributes to final transformation.
Return the output in the following structured JSON format:
"sound sources": ["...", "].."], here you should put the sound sources you are given "complex editing instruction": "...".
"atomic editing steps":
("operation": "add", "target": "...", "cffect": xxx(left, front, right) by xxxdB"),
("operation": "remove", "target": "...". "effect": "None"),
("operation": "turn up/turn down", "target": "...", "effect": "xxxdB"),
["operation": "change", "target": "...", "effect": "to xxx (left, right, or front)"),
"'operation": "add reverb", "target": "...,, "cffect": "(low, mid, or high)"),
("operation": "change timbre", "target": "...", "effeet": "to xxx (bright, dark, warm, cold, muffled)")
NOT break down sound sources into individual words or characters. Sound sources are complete strings and must remain so in the JSON.
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B IMPLEMENTATION DETAILS
B.1 BASELINES IMPLEMENTATION
We present the following baseline methods to evaluate the complex audio editing task. One of the baselines is end-to-end version of Audit without using ALM. All other baseline methods perform multi-step sequential editing with ALM's atomic editing step outputs.
End-to-End Audit. We first train an end-to-end version of Audit that directly predicts the final-step edited audio an conditioned on the original audio dg and Declarative instruction P in one step without ALM. We extend the mono-channel Audit to our binaural setting, where we stack the left and right channels of the mel-spectrograms as the model inputs. Follow the original implementation, we convert 10s of audio into mel-spectrs with a size of 80 624. using a hop size of 256, a window size of 1024, and mel-bins of size 80. We use the same model configurations in the original paper.
SDEdit. SDEdit is zero-shot method that does not require training a new audio editing model. It uses an off-the-shelf text-to-audio (TTA) generation model, which we use Maolee-Adio-Oper as supports binaural audio generation. We use the default 200 total diffusion steps and start the reverse process from a timestep of 100. We use a Mlassifie-frreeguiddee ftree-guiancc (CFG) scale of 7.5. The target caption is composed by concatenating the individual event captions after each editing step.
DDIM Inversion. Similar to SDEdit, we also use Stable PooleeAAddo-Opper as the TTA generation model. We use the default 200 total diffusion steps and start the reverse process from a timestep of 100. We use a CFG scale of 7.5 for both the target and the source. The source text prompt and the target text prompt are composed by concatenating the individual event captions before and after each editing step, respectively.
ZETA. Similar SDEdit and DDIM Inversion, also use Stable-Audio-peen as the TTA generation model. We use the default 200 total diffusion steps and start the reverse process from timestep of 100. We use a CFG scale of 7.5 for the target and CFG scale of I for the source. The source text prompt and the target text prompt are composed by concatenating the individual event captions before and after each editing step, respectively.
AudioEditor:
Specifically in AudioEditor, we replace Affusion with the spatial generator SpatialSonic in BEWO to support binaural editing. We use the default 100 total editing steps with iterations per step to update text embedding for null-text inversion. For the addition task, we use the default punishment ratio (alpha) of -0.001. For the remove or extract task, we use the default punishment ratio (alpha) of 1.
Audit with ALM. To evaluate sequential editing with trainable editor, we use an Audit baseline trained on single-step audio editing data with input audio a--1, atomic instruction 8,, and output edited audio az. The model and data configurations are the same with the end-to-end Audit baseline variant.
B.2 SMARTDJ IMPLEMENTATION
ALM. Our ALM module is initialized from Audio Flamingo This model contains an AF-CLAP audio encoder module to encode the mono-channel audio. The input 10-second audio is first resampled 16KHz and transformed into dense audio features 2, E 1664x2048 The mono-channel CLAP encoder understands the audio semantics and sound events, which is enough to reason the atomic steps to make the edited audio semantically align with the Declarative instruction. This audio encoder is followed by representation transformation layers that expand the model capacity. This module has three self-attention layers to the audio feature representation, each with 8 heads and dimension of 2048. Following this, gated cross-attention layers are used condition audio representations on the LLM. The LLM uses Qwen2.5-3B, a decoder-only causal LLM with 3B parameters, 36 hidden layers, and 16 attention heads. During training, keep both the AF-CLAP and LLM frozen during training. The audio representation transformation layers are fully optimized. We apply LoRA only to the gated cross-attention layers with rank of 16.
DiT architecture We conduct experiments at 24KHz audio sample rate. The waveform latent representation operates at 50Hz and consists of 128 channels. Our DiT model has 24 blocks, each
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with channel of 1024, 16 attention heads, and FFN resulting in total of 597M parameters. During training, we begin with a 50K linear warm-up phase and the result is training with cosine learning rate decay. In the inference, use 100 diffusion steps and a guidance scale of 4, with guidance rescale of 0.8.
Network) dimension of 4096,
C
MORE RESULTS
In this section, provide more qualitative results the ALM-inferenced editing steps and the LDM edited audio results.
C.I RESULTS ON ALM- COME--EEEENCCD ATOMIC EDITING STEPS
We show multiple examples on ALM-inferenced atomic editing steps. In each example, the "audio labels" are the ground truth audio labels for reference and they are not provided to the ALM module. Only the corresponding audios are provided to the ALM. We then input the declarative instruction to the ALM and output the atomic editing steps.
Example I: Make this sound like quiet afternoon garden
Audio labels: clock tick, bird chirp, wind
Declarative instructions: Make this sound like quiet afternoon in garden
Atomic editing steps:
Remove the sound of clock tick
Turn up the the sound of bird tweet by 3dB
Add the sound of gentle breeze at front by 2dB
Example 2: Make this sound like busy city street
Audio labels: engine rev, church bell ring
Declarative instructions: Make this sound like a busy city street
Atomic editing steps:
Remove sound bell ring
Turn down sound engine rev by 2dB
Add sound of distant siren by 2dB
Add sound of traffic noise front by 3dB
Example 3: Make this sound like bustling park on sunny day.
Audio labels: children scream, insect buzz, bird call, chainsaw run
Declarative instructions: Make this sound like bustling park on sunny day.
Atomic editing steps:
Remove the sound of chainsaw
Add the sound of laughter at left by 3dB
Turn down children scream by 2dB
Change the sound of bird call front
Example 4: Make this sound like serene beach
Audio labels: water waves, whistling, music playing
Declarative instructions: Make this sound like serene beach
Atomic editing steps:
Remove the sound of whistling
Turn up the sound of wave crash by 4dB
Add the sound of seagulls calling front by 3dB
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Example 5: Make this sound like busy office
Audio labels: typewriter tap, power tool drill
Declarative instructions: Make this sound like a busy office
Atomic editing steps:
Remove the sound of drilling
Turn up the sound of typewriter type by 2dB
Add the sound of phone ringing at right by 3dB
Example 6: Make this sound like military base
Audio labels: fire truck siren, gunshot, vehicle move
Declarative instructions: Make this sound like military base
Atomic editing steps:
Remove the sound of fire truck siren
Turn up the sound of gunshot by 3dB
Add the sound of helicopter blades right by 2dB
Example 7: Make this sound like calm night in the countryside
Audio labels: snoring, machine run, wind blow
Declarative instructions: Make this sound like calm night in the countryside
Atomic editing steps:
Remove the sound of machine run
Turn down the sound of wind blow by 2dB
Add the sound of owl hooting right by 2dB
Example 8: Make this sound like bustling urban street
Audio labels: horn honk, cat, siren blare, male laugh
Declarative instructions: Make this sound like bustling urban street
Atomic editing steps:
Remove the sound of cat
Turn down the sound of siren blare by 2dB
Add the sound of people chatter at front by 3dB
Add the sound of traffic noise left by 2dB
C.2 FAILURE CASE ON ALM-INEERENCED ATOMIC EDITING STEPS.
To better illustrate how the ALM behaves under abstract conflicting prompts such as "make it sound eerie yet cheerful," we conducted test using this instruction across different audio inputs:
Example I: Make this sound like cerie yet cheerful
Audio labels: child cries, wind blows
Declarative instructions: Make this sound like eerie yet cheerful
Atomic editing steps:
Add the sound of laughing at right by 3dB
Add the sound of person whispers at left by 2dB
Turn up child cries by 2dB
Example 2: Make this sound like eerie yet cheerful
Audio labels: female speech, bell ringing
Declarative instructions: Make this sound like eerie cheerful
Atomic editing steps:
Turn up the sound of female speech by 2dB
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Add the sound of owl call at front by 3dB
Overall, these examples show that the ALM can reliably handle atmosphere-related cues (e.g., "eerie", "cheerful"), but is still challenging task when emotional stylistic attributes conflict or lack clear instruction. This reflects an inherent limitation of mapping abstract emotions to concrete operations. Extending the ALM to better model emotional and stylistic intent is natural direction for future work.
C.3 RESULTS ON ATOMIC EDITING STEPS
Add
Add wind blows right 2 db
then porson taughs at right of db
mecwing the ient of do
few loud shores at front of 2ab
Original
AB
ZETA
Aud:
SmartDj
Figure 9: Examples on add operation. The top row is the original audio and the rest rows are the edited results. Only SmartDJ can keep the original audio clips while adding new audio content.
We provide more atomic editing results on add in Fig 9 As shown by the comparison with the original audio and the edited one, while the baseline method tends to replace the original clips and completely generate a new audio clip, SmartDJ can successfully keep the original contents and add new sound events into it.
We show more editing examples remove and extract in Fig 10 and 12 Compared with baseline method, SmartDJ can effectively either remove unwanted or extract wanted audio contents. The edited results show good alignment with the ground truth edited audios.
Figure II presents additional qualitative results for the change sound direction task. The Y axis in each figure is the sound direction heatmap. SmartDJ consistently relocates the source to the requested spatial direction, and its outputs align well with the ground truth edited audios. By contrast, Audit can alter spatial effect, showing the limitations of using spectrogram audio encoder for direction-awan aware editing.
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Remove burds churping
Remove
Remove rutting, music playing
Remove
remale tnighs, make speates
man speech and explosson
Original
AC
ZETA
Audit
SmartDj
Target
Figure 10: Examples remove operation. The top is the original audio, and the bottom row is the target audio. Only SmartDJ can completely remove unwanted audio parts and keep the remaining part unchanged.
man speating, crinkting to right
man Screariing, man talltng to left
engine sound Pront
white noise, speeon, rustinng to left
Original
11:11
Audit
SmartD]
Target
Figure 11: Examples on change sound direction operation. The top row is the original audio, and the bottom is the target audio. Only SmartDJ can perfectly edit the sound directions that match closely with the target.
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Pxtract
Extract gunttew, stouuhnng
Extract
Extract speesboat
whisting, speaking and leaghing
engine running, hissing
Original
AE
ZETA
Audit
SmantD]
Target
Figure 12: Examples extract operation. The top is the original audio and the bottom row is the target audio. Only SmartDJ can extract wanted audios that are clean and of high quality.
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C.4 HUMAN SUBJECTIVE STUDIES
We provide users with data pairs consisting of the original audio, the editing instruction, the SmartDJ edited result, and the edited results from random competing baseline. In the case of single-step audio editing tasks remove, extract, turn up/down, change sound direction where there exists ground-truth editing solution, we also provide it as the reference audio as shown in Fig. [3b We conduct evaluation, with 19 participants and 20 (10 complex audio editing, 10 single-step editing) data pairs per participant.
Editing change whistling
Audio Pair Editing Instruction: Make this sound like protest in city
male speeth
right
Original Audio
Edited Audio
Edited Audio 2
Original Audio
Reference Audio
Edited Audio
Edited Audio
[[]]]]]]]]]
1000 11000 m100-11
|000011000
5:10
0.00
E000
Audio do fiee align with the original audio the instrction instruction: like protest city
Between Audio and Audio wiich do you feel with the tnigine audio and editng instuutiion betteri instrcct on change the sound of whititing with make speech the the righ
Audio
Audio
quality?
Audio
Audio?
Audio
Audio-
Setween Audio and Aadioo which one do you bether original audie's contenta??
one better
(a) User interface for complex audio editing task
(b) User interface for change direction editing task
Figure 13: The audio pairs, questions, and user interfaces for different audio editing tasks
In the 10 complex audio editing question pairs, we ask the user to select between the SmartDJ edited audio and the edited results from random competing baseline (randomly sampled from AudioEditor, ZETA and Audit) according to the following three questions:
Question list for complex audio editing user study
Between Audio and Audio 2, which one do you feel aligns with the original audio and the editing instruction better?
Between Audio and Audio 2, which one do you feel has the better audio quality?
Between Audio 1 and Audio 2, which one do you feel better preserves some of the original audio's contents?
In the single-step editing for add operation (3 pairs in total), we compare SmartDJ with randomly sampled method from three baselines (A (AudioEditor, ZETA and Audit). We ask the user to answer four questions, with one additional question listed below:
Additional question for add operation user study
Between Audio and Audio 2, which one do you feel the added spatial effect aligns with the text instruction?
For the single-step remove and extract tasks (4 pairs) we compare SmartDJ with a randomly chosen baseline model. For turn-up/urr-down and change direction (3 pairs) we benchmark only against AUDIT, since the other baselines cannot perform these operations. Each pair includes ground-truth reference, and listeners answer the three evaluation questions listed below.
Questions list for the Remove, Extract, Turn up/down, Change sound direction operation user study
Between Audio 1 and Audio 2, which one do you feel aligns with the original audio and the editing instruction better?
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Between Audio I and Audio 2, which one do feel has the better audio quality?
Between Audio 1 and Audio 2, which one do you feel aligns better with the Reference Audio overall?
Across all tasks, SJartDJ is consistently preferred over all baseline methods by analyzing its win rate against three baselines. For the complex audio editing task, SmartDJ receives at least 80% of user votes over the baselines for audio quality, and at least 87% for alignment with the Declarative editing instruction and original audio. This shows that SmartDJ faithfully performs the requested scene transformation while preserving the key elements of the original audio. In the single-step editing task, our method receives more than 77% of user votes for audio quality, and 84% for alignment with the single-step editing text prompt, spatial description, and original or reference audio (when applicable). These results demonstrate that SmartDJ achieves the highest user preference across both quality and alignment metrics, outperforming all competing methods.
Task & Metrie
Baseline
Preference (SmartD.) 95% Confidence Interval
p-value
7ETA
80.00%
[68.9% -1..1%)
0.001
Declarative Edit Quality
AE
95.50%
[90.6% 100.0%]
0.001
Audit
90.40%
[83.7% 97.2%%
0.001
ZETA
87.00%
[80.4% 93.6%)
0,001
Declarative Edit Alignment
AE
91.00%
[86.2% 9
0.001
Audit
93.20%
[89.1% 97.2%%
0.001
ZETA
77.40%
[66.1% 88.6%)
0.001
Single Step
86.30%
[76.8% 95.7%)
0.001
Quality
Audit
95.30%
[90.9% 99.8%)
< 0.001
ZETA
88.90%
[83.4% 94.4%)
0.001
Single Step Alignment
84,90%
[78.7% 91.2%%
0.001
Audit
96.80%
[94.2% 99.3%)
< 0.001
Table 5: User Preference Evaluation Results.
Regarding the statistical significance of the user study results, we calculated it using a two-sided Z-test against a null hypothesis of random chance (50%). A p-value of 0.001 indicates that there is less than a 0.1% probability that SmartDJ's preference rates are due to random luck. This confirms that the preference for our method is statistically significant. While the p-value confirms significance, the Confidence Interval demonstrates reliability. The 95% CI indicates the range in which the true population preference lies with 95% certainty. Because the lower bound our confidence intervals is consistently well above 50% (ranging from 66. 1 %% to 94.2%) we can claim with high certainty that SmartDJ preferred over baselines in the general population.
D USE OF LARGE LANGUAGE MODELS.
Large language models (LLMs) were used support the writing of this manuscript. In particular, we used LLMs to assist with writing fluency, polishing the presentation of technical content, and rephrasing sections improve readability. All scientific insights, technical innovations, and claims presented in this paper are solely the work of the authors. The final content was carefully reviewed, validated, and revised by the authors to ensure both correctness and elarity.
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Reviews:
[{"review_id": "UadpgHuPzu", "reviewer": "Reviewer_bDhL", "summary": "This paper introduces SmartDJ, a novel framework for declarative stereo audio editing. It leverages an Audio Language Model (ALM) to decompose high-level user instructions (e.g., \"make this sound like a sunny forest\") into a sequence of atomic editing operations (e.g., add/remove sounds, adjust volume/direction). These operations are then executed sequentially by a Latent Diffusion Model (LDM) to edit the audio while preserving spatial realism. \n\nThe authors also propose a scalable data synthesis pipeline using LLMs and signal processing to generate paired training data, addressing the lack of such datasets. Key contributions include: (1) the first declarative stereo audio editor combining ALM reasoning with LDM generation; (2) a controllable data pipeline for editable audio scenes; and (3) empirical results showing superior perceptual quality, semantic alignment, and spatial fidelity over baselines like Audit and WavCraft, validated through metrics (e.g., FD, CLAP, FSAD) and human studies.", "questions": "1. How well does SmartDJ generalize to real-world audio recordings (e.g., field recordings with ambient noise or overlaps) versus the synthetic mixtures used in training? Have you tested this, and if not, what challenges do you anticipate?\n\n2. The ALM decomposes instructions effectively in your examples, but how does it handle ambiguous, abstract, or conflicting high-level prompts (e.g., \"make it eerie yet cheerful\")? Could you provide failure cases or metrics for decomposition accuracy?\n\n3. Are there plans to expand the set of atomic operations (e.g., to include reverb, pitch shifting, or event timing adjustments) to support broader editing scenarios?\n\n4. In the human evaluation, how many participants were involved, what was the setup (e.g., A/B preference, rating scales), and what was the statistical significance of the preferences? This could strengthen the subjective claims.\n\n5. Given the modular design, how sensitive is the overall performance to the choice of ALM backbone (e.g., LTU vs. Audio Flamingo)? Did you ablate different ALMs?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper demonstrates strong originality by adapting multimodal large language models (MLLMs) and diffusion-based editing techniques from the vision domain to audio, specifically addressing the underexplored area of declarative (high-level) stereo audio editing. \n\nThe integration of an ALM as a \"planner\" for decomposing instructions into atomic steps, combined with an LDM \"editor,\" is a clever modular design that allows for interpretability and potential human-in-the-loop refinement. The data synthesis pipeline is a standout contribution: using GPT-4o as a \"designer\" to generate instructions and steps, paired with rule-based audio composition, enables scalable training data creation, which is a practical solution to the data scarcity problem in audio editing. \n\nExperiments are comprehensive, including quantitative metrics (e.g., lower FD and higher CLAP scores in Table 1), ablations (e.g., multi-round editing quality in Table 3), and human evaluations showing preference for SmartDJ in fidelity and alignment. Overall, the work is well-substantiated and advances reasoning-guided audio manipulation.", "weaknesses": "While the synthetic data pipeline is innovative, the heavy reliance on composed audio from clean datasets (e.g., AudioCaps, FSD50k) may limit generalization to real-world recordings, which often include overlapping events, noise, or reverberation not fully captured in the synthesis. For instance, the atomic operations are restricted to a predefined set (add/remove/extract/volume/direction), which might not handle more complex edits like timbre changes or temporal alignments, potentially reducing flexibility for diverse instructions. \n\nComputationally, the sequential multi-step editing (e.g., 13.1s inference time in Table 1) is slower than end-to-end baselines, which could hinder real-time applications. \n\nThe baselines are appropriate but could be expanded to include more recent audio editing methods (e.g., adaptations of IP2P or P2P from images to audio, beyond just Audit and WavCraft). Human studies are mentioned but details (e.g., number of participants, inter-rater agreement) are sparse in the main text, making it hard to assess robustness without appendices.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UipUkW3zsy", "reviewer": "Reviewer_eq8D", "summary": "This paper introduces a framework for audio editing that breaks down high-level (declarative) instructions into a series of basic editing steps, using an LDM model trained to carry out these edits. A data synthesis pipeline is employed to create paired datasets for training. Experimental results indicate this approach delivers better perceptual quality and greater alignment with user instructions compared to several baseline methods.", "questions": "Are there any additional atomic operations to consider beyond the current ones?\n\nWhen using Audit as a baseline, do authors use the same training data? If so, what explains the significant subjective preference when comparing SmartDJ and Audit in single-step test?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Innovative use of both Audio Language Models (ALM) and Large Language Models (LLM):\n1. The ALM acts as a planner by understanding input audio, interpreting broad user instructions, and segmenting them into a sequence of simpler editing operations.\n2. The LLM contributes as a designer during data creation, generating a wide range of high-level edit commands and their corresponding atomic steps.\n\nThe study includes comprehensive experiments and ablation analyses.\n\nAllow editing of stereo audio direction.", "weaknesses": "Breaking down user instructions into atomic editing actions isn't a novel concept for audio editing; for instance, WavCraft already transforms commands into several subtasks.\n\nComplicated instructions may not always be easily reducible to a fixed set of basic operations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3K0ry3XeDj", "reviewer": "Reviewer_QgiL", "summary": "This paper presents a framework for high-level audio editing that allows users to modify stereo audio through semantic instructions rather than low-level waveform manipulation. The system introduces an Audio Language Model (ALM) that generates step-by-step editing commands, guiding an audio diffusion model to perform multi-stage modifications until an optimal output is reached. The paper explores the feasibility of such semantic audio editing and evaluates its ability to handle complex instructions such as spatial or environmental sound changes.", "questions": "•  How does the proposed LDM-based editor compare to existing audio separation or editing models in quantitative and perceptual metrics? Only on the editing level, not combining with the ALM guided steps.  \n\n•  What is the dataset used on result table 2, can we compare the results on the same evaluation set used by Audit? \n\n•  Can the ALM component be replaced by an audio-captioning model + LLM prompting pipeline? If not, what specific advantages does ALM bring?\n\n•  Have the authors considered evaluating intermediate steps of ALM-guided editing (e.g., SmartDJ examples) to show how the step-wise process improves the result? Currently the demo only show the comparision on the final results.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "•  The paper addresses a novel and forward-looking task — semantic, high-level audio editing — which goes beyond traditional waveform-level operations.\n\n•  The idea of integrating an Audio Language Model that provides editing steps is creative and can inspire future work combining reasoning and audio generation.\n\n•  The model supports stereo audio editing, which is technically more challenging and rarely explored in prior works.\n\n•  The volume control (±dB) feature is interesting, as most baseline editing systems cannot precisely handle such parametric changes.\n\n•  The overall direction contributes to bridging the gap between natural-language interaction and multi-step sound manipulation, a promising area for interactive audio systems.", "weaknesses": "•  Although the task is conceptually interesting, the training and testing data are artificially concatenated, which raises concerns about the naturalness and generalizability of the results. Real-world editing tasks often involve continuous, unstructured recordings rather than segmented mixtures.\n\n•  The proposed framework mainly focuses on environmental or ambience-level edits (e.g., changing the spatial context or reverb), while many practical audio-editing scenarios involve precise, localized edits (e.g., removing or amplifying a specific sound event), which existing baseline models can already handle effectively.\n\n•  The system architecture resembles a captioning + LLM planning + diffusion editing pipeline. The novelty is somewhat limited since similar modular approaches could be implemented by combining existing captioning and LLM-based reasoning models.\n\n•  The Audio Language Model (ALM) component’s added value is unclear — it might be replaceable by prompting a strong general LLM (e.g., ChatGPT, Llama 3) with audio captions and editing prompts to produce similar step-wise operations.\n\n•  The diffusion-based editor uses a standard DiT backbone without architectural innovation; exploring more modern generative frameworks like flow-matching or bridge-flow models could potentially improve performance and efficiency.\n\n•  Some tables and metrics (e.g., Table 1) are difficult to interpret — it is not clear whether they measure end-to-end editing quality or only the performance of the editor module， especially it seems that there are a lot of new editing instructions generate by the ALM, such as in/decreasing the volume by dB.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nKt8GiPd5a", "reviewer": "Reviewer_NKMZ", "summary": "This paper introduces SmartDJ, a novel framework for stereo audio editing that combines the reasoning capability of audio language models with the generative power of latent diffusion.", "questions": "I will reconsider my score if my concerns are thoroughly addressed.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Novel Task Formulation: The paper introduces declarative editing for stereo audio, an area that remains relatively underexplored and represents a meaningful contribution to the field.\n\n2. Valuable Dataset Contribution: The newly collected SmartDJ dataset provides a solid foundation for training and evaluating complex audio editing tasks, facilitating future research in declarative audio manipulation.\n\n3. Effective Methodology: The proposed ALM-assisted pipeline demonstrates promising performance in handling complex editing scenarios.", "weaknesses": "1. I note that the paper primarily uses mono-channel audio clips for SmartDJ data synthesis. However, it lacks sufficient details on how spatial audio effects—such as binauralization or room acoustics (e.g., reverberation)—are implemented or considered.\n\n2. Due to the cascaded architecture and separate training strategy, the performance of declarative editing heavily relies on the accuracy of the generated atomic instructions. It would be valuable to include quantitative results on the accuracy/feasibility of atomic instruction generation.\n\n3. The connection between the proposed declarative audio editing and precise spatial effects manipulation remains unclear. For instance, in Example 1 (Appendix C.1), the instruction \"Make this sound like a quiet afternoon in a garden\" is fulfilled by adding a gentle breeze, but the spatial direction of this sound seems arbitrary. \n\n4. The novelty of the single-step editing part appears limited, as its training protocol and model architecture closely follow those established in prior works. I am somehow confused.\n\n5. The citation style does not conform to ICLR guidelines; mixing \\citet{} and \\citep{} reduces readability.\n\n6. The only stated distinction between stereo and general audio editing is “direction,” yet only three direction types are considered. More directional categories are expected.\n\n7. The dataset description is insufficient. “We sample 2–5 audio events and use GPT-4o to create 50k training pairs and 1k evaluation pairs of high-level audio editing data to train an audio language model and evaluate the editing pipeline.” This suggests the data are entirely generated and curated by GPT-4o. The evaluation set should be verified by human annotators for accuracy.\n\n8. There is no comparison with existing editing datasets. Given the proposed 50k examples, please provide comparisons for both training and evaluation sets against prior editing datasets.\n\n9. There are actually training-free editing methods, like DDPM inversion or better methods, that can be easily implemented for stereo editing. The authors should compare these models with SmartDJ.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)