Improving Target Sound Extraction via Disentangled Codec Representations with Privileged Knowledge Distillation
Authors: Dail Kim
Joon-Hyuk Chang
Venue: NeurIPS 2025 poster ()
Keywords: Target Sound Extraction
Privileged Knowledge distillation
Disentangled Representation Learning
Neural Audio Codec
Feature-level Knowledge Distillation
Primary area: applications
TL;DR: This paper proposes DCKD, a privileged knowledge distillation framework for target sound extraction that regulates the amount and flow of target information via neural codec and disentangled representation learning.

Abstract: Target sound extraction aims to isolate target sound sources from an input mixture using a target clue to identify the sounds of interest. To address the challenge posed by the wide variety of sounds, recent work has introduced privileged knowledge distillation (PKD), which utilizes privileged information (PI) about the target sound, available only during training. While PKD has shown promise, existing approaches often suffer from overfitting of the teacher model for the overly rich PI and ineffective knowledge transfer to the student model. In this paper, we propose Disentangled Codec Knowledge Distillation (DCKD) to mitigate these issues by regulating the amount and the flow of target sound information within the teacher model. We begin by extracting a compressed representation of the target sound using a neural audio codec to regulate the amount of PI. Disentangled representation learning is then applied to remove class information and extract fine-grained temporal information as PI. Subsequently, an n-hot vector as the class information and the class-independent PI are used to condition the early and later layers of the teacher model, respectively, forming a regulated coarse-to-fine target information flow. The resulting representation is transferred to the student model through feature-level knowledge distillation. Experimental results show that DCKD consistently improves existing methods across model architectures under the multi-target selection condition.

Improving Target Sound Extraction via Disentangled Codec Representations with Privileged Knowledge Distillation
Dail Kim', Joon-Hyuk Chang Department of Artificial Intelligence
*Department of Electronic Engineering
Hanyang University
Seoul, Republic of Korea
{roar27, j jchang) @hanyang... ac kr
Abstract
Target sound extraction aims to isolate target sound sources from input mixture using a target clue to identify the sounds of interest. To address the challenge posed by the wide variety of sounds, recent work has introduced privileged knowledge distillation (PKD), which utilizes privileged information (PI) about the target sound, available only during training. While PKD has shown promise, existing approaches often suffer from overfitting of the teacher model for the overly rich PI and ineffective knowledge transfer the student model. In this paper, we propose Disentangled Codec Knowledge Distillation (DCKD) to mitigate these issues by regulating the amount and the flow of target sound information within the teacher model. We begin by extracting a compressed representation of the target sound using neural audio codec to regulate the amount of PI. Disentangled representation learning is then applied to remove class information and extract fine-gras mne-grained temporal information as Subsequently, an n-hot vector as the class information and the class- mass-indeepedeen PI are used to condition the early and later layers of the teacher model, respectively, forming regulated coarse-to saarse-to-fine target information flow. The resulting representation is transferred to the student model through feature-level knowledge distillation. Experimental results show that DCKD consistently improves existing methods across model architectures under the multi multi-target selection condition.
Introduction
Humans possess a selective hearing ability to focus on specific sound of interest among overlapping sounds. This auditory perception ability has been extensively emulated across various signal pro- cessing studies (155). One major approach to achieve this objective is target sound extraction (TSE), which aims extract or target clues that indicate the classes of target sounds [5H1O]. as illustrated in Figure (a). However, the diversity sound types and acoustic conditions remains a challenge achieving robust performance. To address this issue, recent studies have explored the use of additional information of target sound,
desired sound sources from mixture of multiple sounds, given
such as timestamps [1T] 12]. pitch information [13), and multimodal cues [5), better guide TSE models. A notable work in this direction is the utilization of privileged knowledge distillation (PKD) [12. This work utilizes a teacher model trained with a timestamp of the target sound as
*Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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8,
Student TSE
2 student
Student TSE
student
y
TSE
Teacher
Teacher
KD
Extractor
V
Extructor
Prencher
y
Extractor
Piescher
1,
0
Emb
1
Emb
Pl
1
Emb
Emb
Codec
DSDE
(a) TSE system
(b) PKD framework for TSE
(c) DCKD framework for TSE
Figure I: A block diagrams of (a) baseline TSE system and the PKD framework of (b) prior network
and (c) proposed network for TSE.
privileged information (PI) available only during training and subsequently transfers knowledge to a student model, as shown in Figure 1(b).
While the effectiveness of PKD has been demonstrated in various domains [12 20), there remains substantial room for improvement in how PI is utilized. In particular, highly predictive Pl can lead the teacher overfit to training data, producing high-variance outputs that impair the generalization ability of the student [20). When applied to TSE, this finding indicates that providing overly rich PI to the teacher may cause performance degradation in the student model.
To mitigate this limitation, we propose Disentangled Codec Knowledge Distillation (DCKD), a novel PKD framework for improving TSE. This framework constrains both the amount and the flow of target information by conditioning target class information, provided as a target clue, into the early layers of the teacher model, while class-independent temporal information, used as PI, is conditioned into the later layers (Figure (c)). The amount of PI is regulated by leveraging a pretrained state- of-the-art neural audio codec (21) extract a compressed yet essential representation of the target sound via its encoder and quantization modules. Then, Disentangled representation learning (DRL) is employed to remove class information and extract fine-grained temporal information from the codec representations.
Specifically, the codec representations passed to Disentangled Static & Dynamic Encoder (DSDE), which disentangles the representations into static and dynamic factors using designed encoders for each factor. The disentangled representation is assumed primarily based the static/dynamic factors modeling in [22]. but also reflects the global-local information assumption of voice conversion methods (23). To reinforce disentanglement further, we adopt mutual information (MI)-based strategy. We minimize MI between the static and dynamic factors using the contrastive log-ratio upper bound estimation method (24). and enhance the representation of the static factor by maximizing its MI with the target sound via contrastive lower bound estimation method [52 Meanwhile, the MI between the dynamic factors and the target sound is implicitly maximized through the teacher model training process. Thus, no additional objective is applied to the dynamic factor. The extracted dynamic factors are used condition the later layers of the teacher model, while the target clue conditions the early layers, forming a coarse- fi-f yet regulated conditioning pipeline of target information. This structure allows the teacher model first absorb coarse- level class information and then refine its understanding with paas--ndeependent temporal information from the PI in later stages, thereby avoiding the injection of overly rich PI at once. This design mitigates overfitting problem of teacher model and enables more effective feature-level knowledge transfer to the student model. Experimental results on the Kaggle2018 T-AAU dataset demonstrate that DCKD consistently improves separation performance across various TSE architectures under multi-target selection conditions.
Our key contributions are summarized as follows:
(1) We propose Disentangled Codec Knowledge Distillation (DCKD), a novel privileged knowledge distillation (PKD) framework for target sound extraction that integrates pretrained neural audio codec with disentangled representation learning (DRL) to facilitate compressed yet essential PI extraction and coarse-to-fine target information condition scheme.
(2) We address the teacher overfitting issue in PKD by integrating global-local feature disentangle- ment with DRL, enabling more stable and effective knowledge transfer to the student model.

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(3) Experimental results validate that DCKD consistently improves separation performance across various model architectures, achieving notable gains under acoustically challenging conditions, such as mixtures containing O to 3 target sounds with varying background noise.
2 Related Work
Target Sound Extraction. Target sound extraction (TSE) seeks to isolate a target sound source from audio mixture using user-specified target information provided as target clue. Early approaches utilized n-hot class labels or reference audio as conditioning inputs 6 to the prior speech separation model, Conv-TasNet architecture [26). based these findings, Waveformer [8] was introduced as a real-time streaming model for TSE, demonstrating low-latency performance. More recently, diffusion-basee As TSE can be seen as the inherently combined task of sound classification and source separation, subsequent research has explored various forms of target clues help the model identify target sound, including n-hot class label [5), reference audio enrollment 7) textual queries (27), visual signals [28]. and multimodal embeddings 15) To further enhance TSE performance, several studies have proposed incorporating additional target information. such as timestamps
[9] and state-space-bante based (T0) architectures have also been explored.
[II 12 and
pitch cues [13). which have shown clear benefits. However, these methods often rely on auxiliary DNNs and may yield suboptimal improvements depending on the quality or type of the additional information. In contrast, our approach enhances performance by effectively transferring PI from the teacher to the student model through principled regulation of PI, without increasing inference complexity.
Privileged Knowledge Distillation. Privileged knowledge distillation (PKD) leverages PI available exclusively during training to train a teacher model, which then guides a student model without access to this information (16). This approach has been widely applied across various domains [17 18 29 32).
including audio-related downstream task [17 18 32). due to its ability to utilize privileged information unavailable during inference. However, as noted in [20). providing overly rich PI can cause the teacher model to overfit, leading to ineffective knowledge transfer and degraded student performance. To mitigate this, we employ neural codec to regulate the amount of PI and DRL to enable gradual knowledge transfer. This approach prevents the teacher model from over-relying on privileged information, ensuring effective distillation to the student model.
Disentangled Representation Learning. Disentangled representation learning (DRL) seeks to separate latent representations into distinct and interpretable factors, thereby improving model in- terpretability and generalization [33]. Building on its demonstrated effectiveness across a variety of domains 24 DRL has been applied in speech processing tasks, including voice conver- sion (23). speech representation learning (34), and target speaker extraction (35). Recent studies have extended DRL to neural audio codec representations [36), broadening its applicability to general sound processing. In this work, we leverage DRI to improve the effectiveness of PKD for TSE.
3 Proposed Approach
3.1 Overview
As illustrated in Figure 2. our proposed system consists of two stages:(1) training a teacher model with DRL and (2) training student model via PKD. Given mixture signal y E R of length L, an n-hot label vector E [0, 1 representing the target class(es) among M possible classes, and the target sound x E IR_, the teacher model trained using y as input, as the target clue, and PI derived from I, as illustrated in Figure 2(a). To extract PI, target sound x is first passed through a pretrained neural audio codec encoder followed by a quantization module, resulting in a codec representations C1.1. This representation is then processed by the proposed DSDE, which separates it into a static factor s and a dynamic factors 21:7. following notation of (22). Among disentangled factors, only the dynamic factors are used to condition the latter part of the teacher model. The static factor thus functions solely to enforce the disentanglement, guiding the dynamic factors to retain class-indepent iass-indeeendeet temporal details of the target sound. Conditioning the n-hot vector as target clue at the front and the dynamic factors the back of the teacher model enables regulated, coarse-to-fine flow of target information condition. Once the teacher model is trained, its intermediate feature

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Teacher
Student
DecRAC
Linear
Avgefeel
Outpptt
Nusk extimator
Output
Masa eatimator
Output
DeGE
Finc Info
Squaood
DALL
21.22
Dense
TCN
Liuner
InsNorm
TCN
TCN
ReLU
HALU
LSTM
F
+
Tiwerr
Liuoee
GL:2
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TCN
TCN
Anghooa
21
EucDAC
Emb
Enc
Emb
Enc
Emib
Enc
(a) Training teacher model with DRL
(b) Training student model with DCKD
Figure 2: Overview of the proposed DCKD framework, consisting of (a) teacher model training with DRL and (b) student model training with DCKD. During the teacher model training, the model conditioned the n-hot vector target clue and the disentangled PI extracted from the target sound via a neural audio codec and DSDE. The student model learns from the teacher via feature-level KD, using intermediate latent features. The frozen block is colored in blue.
representations are frozen and used to supervise the student model via a feature-level knowledge distillation loss, as shown in Figure 2(b). This enables the student to learn target information without access PI during inference, thus preserving efficiency while benefiting from additional information.
3.2 Disentangled Codec Representations Learning for Teacher Model
Disentangled Static & Dynamic Encoder using Neural Codec As illustrated Figure 1(a), the proposed DSDE disentangles neural codec representations of the target sound into time-invariant static factor and a time-variant dynamic factor. This is achieved via a static encoder E, and a dynamic encoder Es, each tailored capture the respective characteristics of the factors. The input to DSDE is the codec representations C1-r {cI..... er obtained from pretrained state-of-the-eee art neural codec model DAC (21), where e; e R. denotes the latent codec representations frame index with codec feature dimension D The codec representations extraction can be expressed as:
C1.r Q (EncDAc (x)),
where ERCAAC denotes the encoder of DAC and Q denotes the quantization module.
We assume that the codec representations C1.7 can be disentangled into static factor and dynamic factors Z1.7 under a statistical independence assumption, i.e., p(s, 21.r) = p(s)p(21:r) following prior research [22]. The dynamic factor at each time step i is assumed to be dependent žςi = {zg. ø1, 2 with žμ = 0. Inspired by (23). each frame-wise dynamic factor is assumed to follow standard Gaussian distribution: p(2,) ~ I), to reduce complexity. Based on these assumptions, the posterior distribution of the disentangled representations to be learned can be factorized
g(8, Z = ((slet::)g( = g(s(cr)) II (Zsslzenn
(2)
The static factor is designed to capture iime-invrriant class identity information, which is modeled using a channel attention module (CAM) (37] to emphasize class-specific information across channels. Note that this static factor is not used as input to the teacher model, instead it is used to enforce the disentanglement class-related information from the dynamic factor. The dynamic factors Z1.T modeled to capture sequential, local acoustic details using bi-directional long short-term

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memory (Bi-LSTM) network (38. To remove class information while preserving fine- tie-ggained temporal information, we apply instance normalization (IN). IN has been adopted in style transfer and voice conversion to filter out global style attributes [2 39 40 such illumination and contrast in images [39] or pitch and energy in audio [40), while preserving content information. Based on these findings, we hypothesize that IN in our framework can filter out class-specific global features, such as characteristic pitch or energy patterns associated with certain sound classes. Our ablation studies in table 4 confirm that using IN improves performance, supporting our hypothesis that IN class-related information from the codec representations, thereby
facilitates disentanglement enabling effective application of DCKD. After disentanglement, the static and dynamic factors are concatenated and passed through a linear transformation function f. yielding a reconstructed codec representation, which is decoded using the frozen decoder of the DAC DecDAC, formulated as:
&e Decpac(f(C 21:r)).
The output of decoder is then optimized to reconstruct target sound using the multi-resolution short-time Fourier transform (STFT) loss [41).
Mutual Information-Base Disentanglement We adopt MI-based disentanglement approach to encourage the MI between codec representations and each factor while discourage the MI between disentangled factors. However, noted in (42). disentangled representations can be meaningless without proper inductive biases. To address this, we guide the static factor to retain class-relevant information by minimizing the cosine distance to the embedding vector of the n-hot class label. To minimize the MI between the latent factors I(21,T, we adopt the variational contrastive log-ratio upper bound (vCLUB) 24 estimator, which is proposed as an upper bound estimate of MI. The vCLUB objective derived based on the modeled factors in (4), is formulated as follows:
I(21.2 s) < 1)CLUB(Z1:T.5
(4)
log q( (1
1
Σ
log a( 1:
( (0))
N
T
N log g( (0)
N
(2
(1) (5) log g(
Rsce 12
s s0)l.
N
Ø=1
where N denotes the number of samples and (( (((2)) (1) conditional posterior distribution, estimated using variational approximation network optimized maximize log-likelihood function. The likeliield loss based the modeled factors (4) is formulated as,
Fict (()) is a variational approximation of the
T
N log q( -211 ()).
L. =
(5)
N
1=1 1=1
To further improve the representation of disentangled static factor, we maximize its MI with the target sound via MI-lower bound InfoNCE (25). which contrastive estimation method based on
Noise Contrastive Estimation (NCE) 43 The InfoNCE lower bound for static factor based on
augmentation [2 is defined as,
N
sim(s (0) (0)
1
1(21:188) 2 IntONCE(S, Saug)
log
(6)
N
N D
where sim(:.) means cosine similarity function and Saug is the static factor with content augmentation, generated using the same augmentation strategy as in [22]. We do not apply additional MI maximiza- tion the dynamic factor, as training the teacher model to estimate the target sound implicitly acts as an inductive bias that maximizes MI between the dynamic factors and the target sound.
Objective Formulation for Teacher Training model is defined as:
The total objective function for training the teacher
Leacher LISE + ACLUB(LsCLU LL)) ANCELIOONCE + Acos + LSTFT

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where ĀCLUB, ANCE, Acos are hyperparameters used to balance each objective, and LISE, Leos: LSIFT represent the TSE loss between and Leeacher: the cosine distance between the embedding of the n-hot label ž and and the multi-resolution STFT loss between &eodec and x [1 for the reconstruction. For the TSE loss, we employ a conditional loss function that applies different objectives based on signal activity to consider the presence or absence of target sounds. Specifically, we compute the negative SDR loss [44] for non-zero (active) targets and a log-scale mean squared error for zero (inactive) targets to penalize hallucinated outputs. The TSE loss for each source is defined as:
{ (00 (I|==| log (1x Prencher 11:)
if | >
(Ts &reacher)
{
[10llgg log (I|æ Reaachr E),
otherwise
(8)
where is small constant used as threshold for absence and to prevent numerical instability, which set to 1e-8.
3.3 Disentangled Codec representations Knowledge Distillation for Student Model
After training the teacher model, its privileged knowledge transferred to the student model through feature-level knowledge distillation. Motivated by [45], we compute the mean squared error (MSE) between the corresponding feature representations of the teacher and student models transfer the disentangled knowledge:
feature
(2s, 2,) = || - ((=)),
KD
where 2g and 2, is the feature representation fron student model and teacher model, and g(-) represents linear transformation used to align the dimensions for distillation. This feature- based distillation loss is combined with a result-based knowledge distillation loss which can be expressed as,
result LISE (freacher sstudent) )
(10)
which computes the TSE loss between the outputs of the teacher and student models. To further enhance the effectiveness of knowledge transfer, the student model is initialized with the pretrained weights of the teacher model, followed by fine-tuning. The overall objective function for training student model is formulated as: Lsuueent LTSE + Ase LeL feature + Are LxD
result
(11)
where LISE, Age and Are is TSE loss between Sudden and x are hhper-parametes for the KD objectives.
4 Experiments
4.1 Experimental Setup
Evaluation Metric. We evaluate model performance using cennal-to-distrticn Ratio (SDR) and
Scale-Invariant SDR (SI-SDR) 44 improvements over the mixture signal in decibels (dB) using the BSSeval toolkit 446
Dataset. We constructed a synthetic dataset by com- bining sound events (SEs) from the Freesound Dataset Kaggle 2018 (FSD Kaggle) [47) and background
Table 1: SDR and SI-SDR average of the test set in dB scale.
sounds from the TAU Urban Acoustic Scenes 2019 dataset (48). The FSD Kaggle dataset provides paired SEs and corresponding class labels across M 41 diverse classes. For dataset partitioning, the train, vali- dation, and eval splits of the FSD dataset were used for training, validation, and testing, respectively. Each mix- ture was synthesized by randomly selecting [3, 4,5)
of target SE class
SDR S1-SDR
-6.01
0.57
8.51
0.91
-6.10
0.54
8.50
0.87
SEs from the FSD dataset, each with duration of 1.5 to seconds, and adding them at random time positions onto a 5.9-second (260,000 samples) background sound from the TAU dataset. Within each mixture, (0, 1, 2, 3) SEs were randomly chosen as targets. The signal-to-noise ratios (SNRs) of the SEs relative to the background were randomly sampled between 15 and 25 dB, with the background level fixed at -30 dB. All audio samples are following original sampling rate, 44.1 kHz. During

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training, 2,500 mixtures were generated per epoch through on-the-fly random mixing, For validation and testing, 5,000 fixed mixtures were prepared following the same generation procedure, excluding the zero target sound selection case. Table 1 shows the SDR and SI-SDR of the test set for each number of target SEs.
To validate the generalization capability of DCKD, we further conducted experiments on the ESC-50 dataset [49). ESC-50 consists of 50 sound classes, each containing 40 clips of 5 seconds. The dataset was split into 20, 10, and 10 clips per class for training. validation, and testing, respectively. Following the same procedure described above, we generated 2,500 on- on-the-fly fly mixtures for training and 1,000 fixed mixtures each for validation and testing.
Implementation. We employed the Adam optimizer with an initial learning rate of 5e-4. A learning rate scheduler reduced the learning rate by a factor of 0.9 whenever the validation loss failed to improve for three consecutive epochs. The teacher and student models were trained for 500 and 300 epochs, respectively, with early stopping when the models stopped improving for 30 epochs. The model checkpoint with the lowest validation loss was selected for evaluation. During model optimization, the weighting parameter of teacher loss function, ACLUB, ANCE and Acos were fixed at le-5, 1.0, and 10.0 and for student loss function, Afe and Xre were set to 1e-4 and 0.6 to balance the values of losses. For the experiments comparing different types of PI, we generated timestamp, pitch, and neural codec representations of the target sound. The timestamp information was derived from the known time indices during mixture generation, using a window size of 64 and a stride of 32, consistent with the encoder of baseline model configuration. Pitch information was extracted using the Parselmouth package [50]. We repeated the extracted pitch values to match the frame dimension of the intermediate features of the teacher model. The same alignment procedure was applied to the neural codec representations. The codec feature dimension was set to 1024, as defined by the pretrained model. All types of privileged information were modeled using bidirectional LSTM (Bi-LSTTM) architecture, identical to that used in the dynamic encoder. All experiments were conducted on server equipped with four NVIDIA GeForce RTX 4090 GPUs.
Compared Baseline Configurations. To validate the effect of the proposed DCKD framework across different DNN architectures, we adopted two distinet models: Sound Selector [5]. a widely used baseline for TSE tasks, and Waveformer (8). which was proposed for real-time TSE applications. For the Sound selector, we followed the configuration of (5) composed of repeat of 8 convld ld blocks with learnable encoder-decoder with feature dimension 128, except for the window size, which is 64 adapt to 4k.1. The target clue was conditioned after the first repeat and the dynamic factors was incorporated after the final repeat block. We adapted CAM (37) for the static encoder, adding linear projection layer at the front to match the feature dimensionality and an average pooling layer applied at the output. All pooling operations were implemented as 1D operations along the temporal dimension. To estimate the conditional posterior distribution of the disentangled factors, we jointly train a variational approximation model alongside the teacher model, which consists of four linear layers with ReLUU activations for both the mean and log-variance estimators, followed by a final tanh activation to constrain the variance.
For the Waveformer, we utilized the non-causal version introduced in [8) The stride size for the first ID convolutional layer was set to 32, and the numbers of encoder and decoder layers were configured as 10 and I, respectively, following the original implementation. To facilitate stable optimization of PI, we set the encoder and decoder feature dimensions to 256. To match the performance of Waveformer reported in the original paper, we trained it with 10,000 samples per epoch. The target clue was applied following the same approach as in [8), and the dynamic factors was incorporated similarly to the teacher model of the Sound Selector via element wise multiplication with the output of the mask generator.
4.2 Comparison to Privileged Information
Table presents the performance comparison of the proposed DCKD framework under various types of PI using the Sound Selector architecture. The student model trained without any PI showed the lowest performance, with an SDRi of 8.00 dB and SI-SDRi of 7.30 dB. In contrast, teacher models equipped with different forms of Pl-such as timestamps, pitch, target sound, and neural codec features-cn consistenlyy outperformed the student baseline. Notably, using the raw target sound as PI yielded the highest teacher performance, reaching an SDRi of 16.23 dB.

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However, when these PIs were transferred to the student model via knowledge distillation, differ- ent tendency was observed. Although all Pls led to performance gains over the baseline student, the effectiveness of distillation did not align with the richness of the PI. In particular, compared to methods using timestamps or pitch as PI, richer forms of PI such as the raw target sound yielded limited improvements for the student model rel- ative to codec- based representations. This shows that overly informative PI can limit the perfor- mance gains over the baseline.
Table 2: Comparison privileged information
Type
Mechanism
SDRi
SI-SDRi
None
Student
8.00
7,30
Teacher
11.70
10.94
Timestamp
PKD
9.30
8.46
Teacher
11.05
10.26
Pitch
w/ PKD
9.61
8.71
Target
Teacher
16.23
15.79
Sound
PKD
9.82
8.89
Neural
Teacher
15.78
15.19
For the method employing a compressed target representation obtained via neural audio codec, although the teacher model performance slightly decreased, the corresponding student model out- performed the one trained with the raw target sound, indicating regulated codec information enables more effective knowledge transfer. Furthermore, although the teacher performance dropped further, proposed method achieved the highest student performance with 10.40 dB SDRi and 9.42 dB SI-SDRi. These results demonstrate regulated through codec and DRL enables more effective distillation to the student model.
Codec
w/ PKD
10.25
9.29
Disentangled
Teacher
15.32
14.77
Codec
PKD)
10.40
9.42
4.3
Comparison to Baselines
Table 3: Comparison of the proposed DCKD framework with different baselines.
SI-SDRi
Model
Params (M)
Mechanism
SDRi
SI-SDRi
1-target (A)
2-target (A)
3-target (A)
84.44
Teacher
15.32
14.77
19.96 (7.56)
14.49 (7.23)
9.61 (5.78)
Sound Selector
6.55
Student (5)
8.80
7.90
12.40 (0.00)
7.25 (0.00)
3.83 (0.00)
w/ DCKD
10.40
9.42
14.61 (2.21)
8.55 (1.30)
4.84 (1.01)
81.15
Teacher 10.75
10.21
16.38 (8.46)
11.16 (6.82)
8.60 (4.12)
Wave former
1.67
Student [
5.50
4.67
7.91 (0,00)
4.34 (0.00)
1.59 (0.00)
w/ DCKD
6.48
5.80
9.41 (1.50)
5.31 (0.97)
2.49 (0.90)
Table 3 presents comprehensive comparison between the proposed DCKD framework with teacher- student baselines on two architectures: SoundSelector and Waveformer. Results are reported in terms of SDRi, SI-SDRi, and separation performance under varying target selection scenarios (1-target, 2-target, 3-target). The values in parentheses represent the SI-SDR gain over the student baseline. These results indicate that DCKD consistently improves both architectures architectures regardless of model capacity.
For the Sound selector model, applying DCKD improves the student SDRi from 8.80 dB to 10.40 dB and SI-SDRi from 7.90 dB to 9.42 dB, validating the effectiveness of the proposed framework. In the 1-target condition, DCKD achieves 14.61 dB SI-SDRi, corresponding to +2.21 dB gain over the student model. Similarly, for 2-target and 3-target cases, DCKD provides gains of +1 +1.30 dB and +1.01 dB, respectively. These consistent improvements indicate that DCKD enhances robustness even as separation difficulty increases.
A similar trend is observed with the lightweight Waveformer model. Although the student baseline performance is lower due its smaller capacity (1.67M parameters), DCKD improves SDRi from 5.50 dB to 6.48 dB and SI-SDRi from 4.67 dB to 5.80 dB. The improvements were observed across all conditions -ttarget (+1.50 dB), 2 -ttrgett (+0.97 dB), and 3-target (+0.90 dB) selection tettings-exhibiigg
similar trend to the teacher model. These results demonstrate the robustness of the proposed method, showing that the performance improvements DCKD follow the trends observed in the teacher model. In particular, the improvement observed in the compact Waveformer model highlights the potential of DCKD for lightweight models, aligning with one of the primary objectives of knowledge distillation.
the student model with

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Accuntic quiar
App buise
Bass drum
Burping, enuctatiion
Combell
Glockenspiel
Figure 3: Left: t-SNE visualization of static factor. Right: t-SNE visualization of dynamic factor. The factors are extracted from sound event samples of 10 different sound events from test dataset.
4.4 Ablation Study
Table 4 presents an ablation study evaluating the con- tribution each component in our proposed DCKD framework, including the neural codec, components for DRL and the feature-level knowledge distillation method.
Table 4: Ablation study of DCKD using the SoundSelector model.
Configuration
SDRi
SI-SDRi
Among components, removing the neural codec led to the most significant performance drop (-1.82 dB SDRi), highlighting its critical role providing a compact yet informative representation of the target sound teacher model. This result confirms the importance of compressing PI via pretrained codec before distillation.
DCKD (full)
10.40
9.42
w/o Codec Info
8.58
7.72
w/o L.CLUB
9.88
8.94
w/o LIGNNCE
10.34
9.37
w/o Lcos
10.11
9.15
w/o InstanceNorm
9.61
8.67
w/o MI-based objectives
9.71
8.76
Disabling the MI based objectives, which include the vCLUB, InfoNCE lower bound, and additional inductive biases such as cosine distance loss and instance normalization, consistently degraded per- formance, demonstrating that each objective contributes to effective factor disentanglement and inductive guidance. Notably, removing the entire MI-based objectives led to substantial perfor- mance loss (-0.69 dB SDRi), higlighting the benefit of MI-aware DRL. Interestingly, excluding the entire DRL methods, which is the same as the result of the student model with PKD using codec representations PI, resulted in only moderate degradation (-0.15 dB SDRi), suggesting that DRL provides more robust benefits when with proper inductive bias.
w/o DRL
10.25
9.29
w/o 7 Teature
10.38
9.43
Lastly, we removed the featur-level KD loss at the feature level to assess its effect. Although the improvement may appear marginal the results, we observed that featur-level KD loss enables faster convergence when training student model with DCKD by performing meaningful regularisation during the training process.
4.5
The Analysis of Disentanglement Learning
To evaluate the effectiveness of our inductive biases for disentanglement, we intuitively visualize the learned static and dynamic factors using t-SNE in Figure 3. This visualization helps assess whether each factor captures the intended properties-stee static factor capturing target class identity and dynamic factors capturing class-agnostic temporal details. The left panel of Figure 3 illustrates the t-SNE projection of the static factor, exhibiting clear class-wise grouping. This result indicates that the static factor retains sufficient information to discriminate among class identities, validating the effect of the cosine distance loss in guiding class-related representation learning and InfoNCE loss to preserve information of the target sound.
In contrast, the right panel shows the t-SNE distribution the dynamic factor, which exhibits no evident clustering according to sound class labels. This result is consistent with our objective of removing class identity information from the dynamic factor, preserving only fine grained temporal characteristics.

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These results confirm that the proposed DSDE, combined with the MI-based objectives and additional inductive bias, successfully disentangles the codec representations of the target sound representation into static and dynamic factor.
4.6 Generalization of DCKD
To evaluate the generalization capability of the proposed DCKD framework, we conducted ad- ditional experiments shown in Table The results demonstrate that applying DCKD consistently improves perfor- mance over the baseline student model, validat- ing the robustness and generalizability of the pro- posed method. The relatively low overall perfor- mance and improvement observed in this dataset are likely due to its limited dataset size and the increased number of sound classes (50), which leads to greater inter-class variability that the model must learn.
Table Comparison of student models with and without DCKD on ESC-50 dataset.
ESC-50 dataset,
Model
Mechanism
SDRi
SI-SDRi
Teacher
10.73
10.32
SoundSelector
Student
4.59
3.42
w/ DCKD
4.92
3.62
Conclusion
In this work, we introduced DCKD, novel framework that enhances TSE by leveraging disentangled PI. This framework uses target class information and disentangled class-independen temporal information as a condition for early and later layers of the teacher model, forming coarse-to-fing manner yet regulated information transfer. As the target class information is given as a target clue, approach mainly focuses extracting class- class-independent temporal information from the target sound as a PI for the teacher. is extracted by disentangling the neural codec representations the target sound, guided by an MI-based objective with additional inductive bias. This pre-trained teacher model effectively transfers target-related knowledge to the student model via feature-level knowledge distillation. Experimental results the Kaggle 2018-TAM dataset demonstrate the effectiveness of our method across diverse scenarios. Furthermore, DCKD mitigates the teacher overfitting problem, which is key limitation of PKD approaches, suggesting its potential applicability beyond TSE to other domains.
Limitations and Future work The proposed method has several limitations, First, the use of DAC neural codec introduces a considerable computational increase. Although consistent performance improvements were observed, future research exploring lightweight feature compression models and optimized training procedures for the teacher network to enhance efficiency is needed. Second, the current framework assumes access individual source signals within the mixture during training. This assumption aligns with supervised learning settings but limits applicability to real-world recordings, where isolated target signals may not be available. Therefore, investigating unsupervised or weakly supervised learning strategies to extend DCKD to more practical scenarios can be a key direction for future work.
Acknowledgments and Disclosure of Funding
This work was partly supported by the National Research Foundation of Korea(NRF) grant funded by the Korea MMVI) TRS-2022-00557944
Institute of Information communications Technology Planning Evaluation (ITTP) grant funded by the Korea government(MSIT) (No.RS- 2025-25443882, Bringing "Her" into the real life: Implementation of the voice Al system enabling sentient conversation) and Institute Information communications Technology Planning Evaluation (ITTP) grant funded by the Korea covernment(MSTT) (N0.RS-2020-11201333 Artificial Intelligence Graduate School Program(Hanyarg University)).
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Question:
Answer: [NA]

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Justification:
Guidelines:
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A Implementation Details
Integration of Course & Fine Information The n-hot label is injected into the right after the learaalle encoder of the model via Element-wise multiplication, where the label vector is first linearly projected to match the channel dimension of the latent feature. The dynamic factors are injected into the latter part through element-wise multiplication, specifically right before the output layer, which consists of ID convolution layer.
Computational Efficiency Training time is measured in wall-clock time. The increase in DCKD primarily results from additional forward passes through the teacher model and computation of mutual information-based loss terms.
Table 6: Model size and training time comparison of teacher and student models.
Configuration
Params (M)
Training Time
GPU Setup
Teacher model
84.44
days
4 RTX 4090
Student (baseline)
6.55
9 hours
4 X RTX 4090
Student w/ DCKD
6.55
day (~2.7x baseline)
4 RTX 4090
Implementation Details of Ablation Experiments For the ablation study, we used the same teacher and student model architectures when evaluating the effects of feature-level KD loss (Leenture and the cosine distance loss (Leus).
To validate the effectiveness of the vCLUB method, we excluded L.CLUB. L... and variational approximation model used for modeling the conditional posterior distribution.
For the MI-hased approach ablation, we excluded all MI-based losses: L.cLuD, LuL and LINIANCE to isolate the impact of the MI-based disentanglement strategy.
To assess the contribution of the codec representations, we replaced the codee model with a simple encoder-decoder architecture consisting of a ID convolutional layer and transposed ID convolu- tional layer. Both the encoder and decoder employed a kernel size of 512, a stride of 256, and a feature dimension of 128.
B Ablation Study on Waveformer
Table 7 presents the ablation study results of the Waveformer model, evaluating the effect of each component in the proposed DCKD framework. In most cases, the Waveformer model shows clear performance degradation below the baseline stu- dent model, unlike the SoundSelector-basee abla- tion study, where most configurations still outper- formed the baseline. Given that Waveformer has lower model capacity than SoundSelector, this degra- dation suggests that excessive PI interferes with the convergence of lightweight models. Interestingly, the model without MI-based objectives achieves bet- ter performance than the model with DCKD. These results can be explained by the fact that MI estima- tion increases optimization difficulty and may intro- duce instability, particularly compact models such as Waveformer. As result, using only the Cosine similarity loss (Cos) and Instance Normalization (IN) for DRL. can lead to more stable training and improved performance in such lightweight architectures. Mcanwhile, removing the L feature term LKD
Table 7: Ablation study of DCKD using the Waveformer model.
Configuration
SDRi
SI-SDRi
w/ DCKD (full)
6.48
5.80
w/o Codec Info
4.04
3.11
w/o LVCLAR
4.13
3.37
w/o CISANCE
5.39
4.73
w/o Co..
3.00
w/o InstanceNorm
3.92
3.21
w/o MI-hased objectives
6.52
5.82
w/o DRL
4.98
4.09
w/o leaure
6.41
5.68

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results in only slight performance drop (6.41 dB / 5.68 dB), following a similar trend to that observed in SoundSelector.
C Results on language-query based TSE model
Table 8 shows the results of applying DCKD to the language query-basee TSE model, AudioSep [51).
Table Performance comparison of AudioSep models
Implementation We used the AudioCap dataset
[2] for training, and the lest set sourced from [5T]. All configurations were kept identical to the original AudioSep setup, except for the sampling rate. While
Model
Mechanism
SDRi
SI-SDRi
student
6.97
AudioSep
AudioSep originally operated on 32 kHz audio, the
w/ DCKD
6.96
5.60
sampling rate was set to 44.1 kHz to match the oper- ational range of the DAC, and the model trained accordingly. For evaluation, the test upsampled to 44.1 kHz.
The performance showed slight improvement in SI-SDRi but remained lower in SDRi. This marginal gain can be attributed to two main factors. First, the caption text often includes not only coarse-level semantic information but also additional temporal cues (e.g., "then," "continuous") which may have interfered with the intended functioning models that condition label information only once the early stage, AudioSep conditions the query information throughout the network using FiLM layers. During teacher model training, fine-grained information conditioned the final stage by modulating the feature representations, which cannot be considered as coarse-to-fine conditioning scheme. Therefore, the limited performance gain is likely due the structural difference between the conditioning mechanism of the AudioSep and the intended design of DCKD.
Results
DCKD. Second, unlike the baseline
D Class-Wise Visualization Result
Figure presents the SI-SDR results for 41 target sound event (SE) classes, comparing the input mixture, the baseline Sound Selector, and the Sound Selector with DCKD applied. Our method consistently outperforms the baseline across most classes, except for "Knock" and "Scissors". In particular, substantial improvements drum," and "Saxophone." These results demonstrate the robustness and effectiveness of the proposed framework in enhancing extraction performance across diverse range of target sound classes.
observed for the classes such "Tambourine," " "Bass
E
Sample Spectrograms for Multi-Targel Class Selection Scenarios
Figures 5, 6, 7 and 8 present sample spectrogram visualizations. Each figure includes the input mixture, the output of the baseline Sound Selector, the output of the bascline with DCKD applied, the output of the teacher model, and the oracle target signal. These visualizations are provided under varying numbers target class selections, demonstrating robust improvement over the baseline in both interference sound suppression and target sound detection performance. Example audio files corresponding to the figures are included in the supplementary zip file.

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Impect
Beseline
Proposed
20
10
5DR (88)
-10
Aomustic Dand
Applande
Burping ensetationn
Bark
Boss dhum
Bug
Cella
Chime
Compateer Jorbourd
Circiver
Gough
Cowtbedl
Dremee opart de close Electre
Dovill, bing
Part
caltte sprting
Figure 4: SI-SDR (dB) for each sound event (SE) class, comparing the input mixture, the baseline Sound Selector, and the Sound Selector with proposed method applied. Error bars indicate 95% confidence intervals computed across evaluation samples.
Matp
720
5100
12
544
1200
5000
Te Repooed
Ticcoal Reppoed
20000
2009
1o00
somu
rea
roc
1.2
Time
.200
(a)
(b)
Figure 5: Spectrogram comparisons under I Ittarget selection scenario. (a) shows a case where the proposed method achieves improved suppression of interfering sources, while (b) highlights enhanced detection the target sound

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Vata
2000
2000
1000
10000
1980
1009
12
1.2
Prepored
hoppoed
2200.
2000
500
5000
15000
12
34
Teroo
18
5.4
32
Texool
48
2000
2000
5000
Sua
(b)
Figure 6: Spectrogram comparisons under 2-target selection scenario. (a) shows a case where the proposed method achieves improved suppression of interfering sources, while (b) highlights enhanced detection of the target sound.
Motun
Moruu
zo00
2000
soeu
1ime
Prppoidd
Reppood
20000
2009
2000
12000
1590
1000
12
3.8
38
12
Texrol
Barget
7240
12086
500
5000
(b)
Figure 7: Spectrogram comparisons under a 3-target selection scenario. (a) shows a case where the proposed method achieves improved suppression of interfering sources, while (b) highlights enhanced detection the target sound

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v.0
X0 Brickig
.002
3509
1500
300
30
Tw
Teacher
Trat
200
1300
nic3
Time 30
(b)
Figure 8: Spectrogram comparisons under target sound overlapping with interfering sources. (a) shows 2-target selection case, and (b) shows a 3-target selection case. both scenarios, the proposed method demonstrates improved suppression of overlapping interference compared to the baseline.


Reviews:
[{"review_id": "43jFHCd3OT", "reviewer": "Reviewer_Vu2K", "summary": "This paper introduces a novel Disentangled Codec Knowledge Distillation (DCKD) framework for target sound extraction (TSE). TSE is the task of isolating a specific sound source from a mixture given a clue about the target (e.g., class label or reference). The authors leverage privileged knowledge distillation (PKD) – a strategy where a teacher model has access to additional information (privileged information, PI) during training that the student model doesn’t get at test time. The key idea in DCKD is to carefully regulate the amount and flow of target information provided to the teacher: they compress the target audio through a neural audio codec to limit information, and apply disentangled representation learning (DRL) to separate class-related vs. fine-grained details. The teacher model is trained with both the usual target class label (an n-hot vector indicating which sound classes to extract) and the disentangled codec representation of the target audio as PI. The student model, which at test time only receives the class label, learns from the teacher via feature-level knowledge distillation. Experiments on a multi-target sound extraction dataset (synthetic mixtures from FSD Kaggle2018 + TAU Urban Acoustic Scenes 2019) show that DCKD yields consistent improvement in signal-to-distortion ratio (SDR) for the student model across different architectures and difficult scenarios. Notably, DCKD outperforms prior PKD approaches that use richer PI (like full target audio) by avoiding teacher overfitting and achieving better student generalization. \n\nIn summary, the DCKD approach integrates these pieces: privileged info in the form of a compressed, disentangled target audio representation, delivered to the teacher in a coarse-to-fine manner and then apply feature level knowledge distillation to train the student.", "questions": "The paper should better explain how the privileged information is integrated into the teacher model. Currently, it’s only stated that the n-hot label conditions early layers and the disentangled dynamic factors condition later layers. However, it’s unclear how this conditioning is implemented. The architecture details should be cleared in the supplementary material for the paper if not in the main paper to share the details. \n\nThe authors themselves note that the use of DAC introduces a significant computational increase. It would be great if authors can detail the overhead from model size, training time details and discuss the computational requirements.  For instance, how much longer (in epochs or wall-clock time) does training DCKD take compared to a baseline? Was the teacher model significantly larger in parameters or just in input dimension? \n\nThe proposed PKD framework assumes access to isolated target audio examples for every sound class during training. In the synthetic experiment this is feasible (mixtures are generated with known sources), but in real-world scenarios it might be hard to obtain clean target samples for every sound of interest. For example, is it assumed that one has a collection of prototypical sounds for each class (as in FSD Kaggle) to use as PI? If so, the authors should clarify this and perhaps note that the method is limited to closed-set target classes. Relatedly, how well would the student model generalize to a new sound class that was not seen in training or had no privileged example? Since the student only uses class labels, in principle it could handle new classes if trained appropriately – but without a teacher for those, it’s unclear.", "limitations": "The paper itself notes one main limitation: the reliance on the heavy DAC (neural codec) and the additional training cost. A future direction they plan is to design a lightweight feature compression method to serve the role of DAC. Perhaps a smaller autoencoder trained jointly could replace the off-the-shelf codec, or dimensionality reduction techniques could be used to create a bottleneck without a full codec.\n\nIt would also be interesting to apply DCKD to speech domain explicitly (target speaker extraction). In that case, the privileged info could be a clean recording of the target speaker’s voice. Actually, that scenario is common (speaker extraction often assumes a reference voice recording is available at runtime). But if we assume a case where at runtime you only know the speaker’s identity or name (not a voice snippet), one could train a teacher with a reference voice audio (privileged) and then distill a student that only needs speaker ID (like a class label). This mirrors what DCKD does (because in DCKD student only gets class label, teacher heard the actual voice). This could allow speaker extraction without needing enrollment audio at test time, which is a powerful capability. The technique would be largely the same (codec + DSDE on the voice). This cross-application potential is worth exploration.\n\nReal world application: The dataset here is synthetic. Real recordings might have more variation (reverberation, non-additive noise, etc.). The method might need to prove itself on real sound separation benchmarks (though few exist for arbitrary classes). Possibly, using datasets like AVE (Audio-Visual Event) or overlapping ESC-50 might be a next step. The reliance on a known set of classes and available example recordings per class is a constraint – expanding beyond class-conditioned separation to, say, text-based queries (like “extract the sound of glass breaking”) could be a future extension. Perhaps a language model could generate an “imagined” audio representation as privileged info for a teacher, but that’s quite speculative.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths: \n\nThis work extends PKD to a new domain (general sound separation) with a much richer form of PI (actual audio content) than prior TSE studies. The coarse-to-fine conditioning strategy is insightful it acknowledges that not all information should be dumped in at once. By structuring the flow (class clue first, then details), the teacher’s utilization of privileged data is more controlled. This is a non-trivial improvement over earlier PKD approaches that might simply concatenate an extra feature and risk overfitting. \n\nThe authors validate DCKD on two model architectures and different numbers of target sources. The consistent gains (approximately +1 to +2 dB SI-SDRi across the board) show the method is not tied to a specific network or scenario. This implies the approach will be robust under a range of scenarios. Moreover, demonstrating improvement even for a lightweight real-time model (Waveformer) is important: it indicates DCKD can help in practical settings where computational resources are limited. \n\nThe inference overhead is also low since student model has the same input requirements and the model size is same as the TSE model where the approach improves the performance without increasing the runtime cost or the model size for the deployment. \n\nWeakness: \n\nComputational inefficiency: The DCKD framework is quite complex. It requires training a pretrained neural codec, a DSDE module with multiple loss terms and and a large teacher model, followed by a student. This is overall computationally heavy in case the models are not available. The teacher training runs for up to 500 epochs. In comparison, simpler PKD methods (like using just a timestamp) are much easier to implement and train. The authors acknowledge the computational inefficiency as a limitation and propose to work on a lightweight compression model in future. \n\nThe privileged data used here is specifically audio of the target source itself. This is a strong assumption – essentially, one needs isolated target recordings for training as PI (which they simulate by knowing the mix composition). In real applications, obtaining a clean example of the exact sound one wants to extract (for training) may not be straightforward. By contrast, using timestamps or class names as PI (like prior works) doesn’t require providing the sound itself. The paper demonstrates the superiority of using the target audio as PI (via codec) over those simpler PI types but it doesn’t solve how to get such audio in a realistic setting unless synthetic data or extra sensors are available. If one has a library of isolated sounds for each class, one could use that, but then generalizing to new sounds not in the library might be an issue. In real life, training with real mixtures might not come with ground-truth isolated tracks unless it’s a task like speech where data can be simulated. This is not a flaw in the paper but rather a technical limitation on the application of this method. Would be great to see author's comments on this aspect. \n\nClarification needed: The authors did not report standard deviations or statistical significance. Given that they use a fixed test set of 5000 mixtures, this is fine, but some indication of variance (perhaps via multiple runs) could strengthen confidence. In the NeurIPS checklist, they marked that they reported statistical significance, but in the paper’s content, we mainly see mean SDRi. However, since improvements are fairly large relative to any plausible variance, this is a minor issue.", "was_revised": true, "final_justification": "The authors' rebuttal and follow-up have thoroughly addressed my concerns regarding PI integration, computational details, and assumptions/limitations on isolated target audio and generalization. The clarifications on training overhead and future directions (e.g., lightweight compression, weak supervision, cross-domain applications) mitigate the weaknesses I noted, while reinforcing the strengths of the DCKD framework's novelty, validation across architectures, and practical inference efficiency.  Additionally, the authors' commitments to including new experiments on ESC-50, further baselines like AudioSep, and expanded discussions on limitations (e.g., closed-set assumptions, real-world applicability) address points raised by other reviewers, contributing to a more robust revised manuscript.\n\nThis is a technically solid paper with good impact in audio processing sub-areas, good evaluation, and no ethical issues. I stand by my original accept recommendation.", "rebuttal": "# Response to Reviewer Vu2K\n\n\nWe are very grateful for your valuable comments and suggestions. Below is our response to your review:\n\n**Q1. The paper should better explain how the privileged information is integrated into the teacher model.**\n\nThank you for the comment. \nThe n-hot label is injected into the right after the learnable encoder of the model via element-wise multiplication, where the label vector is first linearly projected to match the channel dimension of the latent feature. The dynamic factors are injected into the latter part through element-wise multiplication, specifically right before the output layer, which consists of a conv1d layer. Thank you again for highlighting this important clarification. We will clarify this in the revised manuscript.\n\n**Q2. how much longer (in epochs or wall-clock time) does training DCKD take compared to a baseline? Was the teacher model significantly larger in parameters or just in input dimension?**\n\nThank you for the suggestion. We will elaborate on this part in the revised manuscript.\n\n1. Model Size: As we reported in Table 3, the parameter size of teacher model is increased due to DAC parameter size, about 80M.  \n2. Training Time:\nWall-clock time increases when training a student with DCKD by approximately 2.7 times compared to training a baseline student model alone, primarily due to the additional forward passes through the teacher model and the computation of the mutual information-based loss terms.\n\n    - Teacher model: 3 days \n    - Student model: 9 hours  \n    - Student with DCKD: 1 day \n\n      on 4 NVIDIA GeForce RTX 4090 GPUs.\n\n**Q3. Is it assumed that one has a collection of prototypical sounds for each class (as in FSD Kaggle) to use as PI? If so, the authors should clarify this and perhaps note that the method is limited to closed-set target classes. Relatedly, how well would the student model generalize to a new sound class that was not seen in training or had no privileged example? Since the student only uses class labels, in principle it could handle new classes if trained appropriately – but without a teacher for those, it’s unclear.**\n\n *About the reference audio during training*\n\nThank you for your insightful comment. Yes. Our framework assumes access to reference audio for each mixture during training. This assumption aligns with the common practice in the target sound extraction (TSE) domain, where supervised training typically requires isolated target signals as ground truth. As you mentioned,  this is a limitation of the general supervised learning paradigm for sound extraction, rather than being specific to our method.\n\nWe agree that performing training with real mixtures without access to target recordings is practically important challenge. In such cases, we believe a feasible solution would be to pretrain the model using a synthetic dataset that includes the target sound class and is constructed to resemble real-world acoustic conditions as closely as possible. The pretrained model can then be deployed for inference on real mixtures.\n\nFurthermore, [1] has demonstrated that sound separation can be learned under weak supervision using only frame-level or clip-level labels. This type of weak PI could be incorporated into our framework in future work.\nMoreover, your suggestion to explore other forms of PI, such as text-based descriptions or language model-derived embeddings, is also inspiring. \n\n> [1] F. Pishdadian, G. Wichern, and J. Le Roux, “Finding Strength in Weakness: Learning to Separate Sounds With Weak Supervision,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2386–2399, 2020.\n\n*Regarding generalization*\n\nAs the current model is conditioned on a fixed set of 41 predefined classes, the input channel size of the class embedding layer is correspondingly fixed. Therefore, to generalize to new classes not seen during training, the model architecture needs to be modified and fine-tuned accordingly. This is an inherent limitation of class-conditioned models. Exploring more flexible conditioning mechanisms—such as continuous or text-based queries—that could allow generalization beyond fixed class sets would be interesting future work.\n\n\n**Future directions: lightweight feature compression instead of DAC, domain trasformation (target speaker extraction), Real world application, text-based queries**\n\nThank you for the suggestions. We are actively exploring the directions you mentioned."}, {"review_id": "mWXM8rvVoX", "reviewer": "Reviewer_TrbU", "summary": "The paper proposes a method for Target Sound Extraction in a PKD frameword that overcomes the possibility of overfitting that usually happens in PKD. The key contributions are generating a disentangled representation of the target sound. The disentanglement also acts as a regularization that helps avoid overfitting. In order to learn meaningful disentanglement, mutual information objectives are used.", "questions": "1. What makes this problem definition only limited to the scope of TSE? Is the solution generalizable to any domain or are we making certain assumptions to make the solution work only for TSE.\n2. What is the effect of the codec model on the performance? It would be nice to analyze a variety of neural codec models and understand its impact.", "limitations": "I was not able to find a limitations section in the paper. In the checklist, it is mentioned that section 6 has the limitations, but I am not able to find a section 6.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The problem is well-motivated and the proposed solution is explained clearly. Overall the paper is well-written and easy to understand.\n2. The authors provide ablation studies explaining the effect of each of the proposed component.\n3. The problem the paper is trying to solve is significant: reducing overfitting in PKD\n4. Although the proposed methodologies are not novel, it is a good solution to the problem in hand.\n\nWeakness:\n1. The authors do not compare the proposed method with other TSE models/baselines from related work. From my understanding, table 3 only compares distillation based methods w/wo the proposed solution.\n2. The experimental section is also limited to one synthetic dataset that the authors curate. To understand the true potential of this model, we need to experiment with more datasets.", "was_revised": true, "final_justification": "I would like to stand by my original decision of borderline accept.  My primary concerns with the paper were the limited datasets and the lack of comparison to other baselines. The authors in the rebuttal mentioned that more datasets will be added in the revised paper. Although they mentioned that additional baselines is not feasible, this paper has enough contributions to justify a borderline accept.", "rebuttal": "# Response to Reviewer TrbU\n\nThank you for your insightful feedbacks. Below, we provide responses to each of your points.\n\n**Weakness1: table 3 only compares distillation based methods w/wo the proposed solution.**\n\nThank you for your valuable feedback. We understand the importance of situating our proposed method in the broader context of prior work.\n\nTo clarify:\nThe “student” models presented in Table 3 are based on existing TSE architectures—SoundSelector and Waveformer—which were proposed in prior works. The “student” entries correspond to the original TSE baselines without any knowledge distillation or privileged information, while the “w/ DCKD” entries reflect performance after applying our proposed method to those existing models.\n\nThus, Table 3 already shows that our method provides consistent improvements over established, non-distilled TSE baselines across model types and mixture conditions. We chose these particular models as baselines because they are representative, widely-used, and lightweight enough to be distilled effectively.\nWe will revise the Table 3 to include citations for these models.\n\n**Weakness2: The experimental section is also limited to one synthetic dataset that the authors curate.**\n\nTo address the concern, we are currently conducting additional experiments on the ESC-50 dataset [1]. ESC-50 contains 50 sound classes, each with 40 clips (5 seconds each). We split the dataset into 20/10/10 clips per class for training, validation, and test, respectively. Using these, we constructed 1,000 fixed mixtures each for validation and testing. The preliminary results (at epoch 91) show that applying DCKD consistently improves performance over the baseline. These results strengthen the validity of our method. We will include the finalized ESC-50 results and further discussion in the revised manuscript.\n\n|                | SDRi (DB) | SI-SDRi (DB) |\n|:--------------:|:---------:|:------------:|\n|     Student    |    4.55   |     3.47     |\n| Student w DCKD |      4.67     |       3.50       |\n\n> [1] K. J. Piczak, “ESC: dataset for environmental sound classification,” in Proc. ACM Multimed., 2015.\n\n**Q1. What makes this problem definition only limited to the scope of TSE? Is the solution generalizable to any domain or are we making certain assumptions to make the solution work only for TSE.**\n\nThank you for your insightful question. While our proposed method was developed and evaluated in the context of Target Sound Extraction (TSE), we believe the core idea behind our method is not inherently limited to this domain.\n\nDisentangled Codec-based Knowledge Distillation (DCKD) is motivated by a key insight:\nWhen applying Privileged Knowledge Distillation (PKD), providing overly rich or entangled privileged information (PI) all at once can lead to suboptimal student learning. Instead, regulating the flow of PI by disentangling it into static (e.g., class-info) and dynamic (e.g., temporal info) factors, and injecting them at early and later of the teacher model, allows for more effective supervision.\n\nWe believe this strategy is not specific to sound classes—it reflects a general principle: progressive and structured use of privileged information improves distillation.\nFor instance:\n\n- In *target speaker extraction*, a target speaker’s utterance can be used as PI, disentangled into speaker identity and temporal dynamics.\n\n- In *audio-visual separation*, video frames could provide PI with spatial-static and motion-dynamic cues.\n\n- In *cross-modal generation or retrieval*, language descriptions could be disentangled into semantics, syntax, or style components.\n\nWe will elaborate on this generalization potential in the revised version. Thank you again for pointing out this important aspect.\n\n**Q2. What is the effect of the codec model on the performance? It would be nice to analyze a variety of neural codec models and understand its impact.**\n\nWe appreciate the suggestion to analyze the influence of different codec models. In our current work, we adopted the DAC codec primarily due to its strong reconstruction quality and publicly available pretrained model, which allowed us to focus on evaluating the DCKD framework itself.\n\nWe fully agree that the choice of codec may significantly affect the type and fidelity of information passed as privileged input to the teacher model. For instance, lower-bitrate codecs may yield more compressed but semantically informative features, while high-bitrate codecs may have fine-grained details. Understanding the effect on DCKD performance of this trade-off would be helpful.\n\nDue to limited resources, we were not able to conduct a comprehensive study across multiple codecs within this submission. However, we recognize this as a key direction for future work.\n\n**Limitations: I was not able to find a limitations section in the paper. In the checklist, it is mentioned that section 6 has the limitations, but I am not able to find a section 6.**\n\nThank you for pointing this typo. Limitation is included in Section 5 (Conclusion)."}, {"review_id": "75C1BU7PUA", "reviewer": "Reviewer_dJGw", "summary": "This papers propose a novel privileged knowledge distillation framework for target sound extraction. The authors proposes a coarse-to-fine conditioning mechanism to mitigate overfitting by providing class-level information to earlier layers and class-independent information to later layers. The experimental results demonstrates the effectiveness of the proposed framework in improving the performance of the SoundSelector-based and Waveformer-based student model.", "questions": "- (Line 73) \"achieving notable gains under acoustically challenging conditions.\" -> It's unclear what \"acoustically challenging conditions\" mean here. Please be specific.\n- (Figure 2) It would be helpful to highlight which components are being trained and which are frozen.\n- (Line 111) $l \\in \\mathbb{R}^M$ -> Should it be $l \\in [0, 1]^M$ to be more precise?\n- (Line 146-147) \"To further remove class information while preserving fine-grained temporal information, we apply instance normalization,\" -> It is not straightforward to me how instance normalization can remove class information. Please clarify this.\n- (Line 156-157) \"To address this, we guide the static factor to retain class-relevant information by minimizing the cosine distance to the embedding vector of the n-hot class label.\" -> If I understand it correctly, this will make the static factor into class-level features rather than instance-level features, unlike the claimed \"temporal-variant\" vs \"temporal-invariant\" features.\n- (Table 2) How about the Waveformer architecture? Did you observe a similar trend?\n- (Line 331) \"Lastly, the removal of feature-level KD loss led to non-trivial performance drops,\" -> Is this true? The numbers are super close (10.40/9.42 vs 10.40/9.43). Am I missing something here?\n- (Line 360-361) \"DRL contribute significantly to improving performance over the baseline.\" -> Please weaken this claim as there's no significance test.", "limitations": "Discussion on the limitations is missing. -> The authors have promised to extend the discussion on the limitations in the revised manuscript.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- The thorough ablation study demonstrates the necessities of each proposed component.\n- The experiment results are well-organized and clearly explained.\n\n### Weaknesses\n\n- The proposed method was only evaluated on one dataset and one backbone model for Table 2 and 4 (two backbone models for Table 3). Additional experiments on another dataset would help examine the generalizability of the proposed framework.\n- The error bars are missing, making it hard to see if any of these differences are significant.\n- The effectiveness of the feature-level KD loss is unclear as the performance remains almost the same when $L_{KD}^{feature}$ is removed.", "was_revised": true, "final_justification": "The paper is overall well-written with some minor writing issues. The rebuttal has addressed my main concerns. I am thus recommending accepting this paper.", "rebuttal": "# Response to Reviewer dJGw\n\nThank you for insightful and constructive reviews. The below is our response:\n\n**Weakness1: The proposed method was only evaluated on one dataset and one backbone model for Table 2 and 4 (two backbone models for Table 3).**\n\nTo address the concern, we are currently conducting additional experiments on the ESC-50 dataset [1]. ESC-50 contains 50 sound classes, each with 40 clips (5 seconds each). We split the dataset into 20/10/10 clips per class for training, validation, and test, respectively. Using these, we constructed 1,000 fixed mixtures each for validation and testing. The preliminary results (at epoch 91) show that applying DCKD consistently improves performance over the baseline. These results strengthen the validity of our method. We will include the finalized ESC-50 results and further discussion in the revised manuscript.\n\n|                | SDRi (DB) | SI-SDRi (DB) |\n|:--------------:|:---------:|:------------:|\n|     Student    |    4.55   |     3.47     |\n| Student w DCKD |      4.67     |       3.50       |\n\n> [1] K. J. Piczak, “ESC: dataset for environmental sound classification,” in Proc. ACM Multimed., 2015.\n\n**Weakness2: The error bars are missing, making it hard to see if any of these differences are significant.** \n\nWe appreciate the suggestion. We will revise the figures to include error bars for all quantitative results to improve clarity.\n\n**Weakness3: The effectiveness of the feature-level KD loss is unclear as the performance remains almost the same when L_kd^feature is removed.**\n\nWe will revise the text, weakening the original claims.\n\nAlthough the improvement appears modest, the feature-level KD loss serves as a meaningful regularizer during training. Specifically, it encourages alignment between the intermediate representations of the student and the teacher, which helps stabilize learning dynamics and facilitates faster convergence. For example, we observed that the model with feature-level loss converged at epoch 287, compared to epoch 296 without it.\nWe acknowledge that the improvement in final metrics is not substantial enough to justify claims. We will revise the text accordingly.\n\n**Questions:**\n\n**(Line 73) \"achieving notable gains under acoustically challenging conditions.\" -> It's unclear what \"acoustically challenging conditions\" mean here. Please be specific.**\n\nThank you for pointing this out. We will revise the sentence for clarity:\n→ “achieving notable gains under acoustically challenging conditions, such as mixtures containing 0 to 3 target sounds with varying background noise.”\n\n**(Figure 2) It would be helpful to highlight which components are being trained and which are frozen.**\n\nWe are very thank you for pointing this out. We will revise Figure 2 to visually indicate frozen and trainable components. Specifically, modules shaded in blue are frozen during training.\n\n**(Line 111)**\n\nThank you for the suggestion. we will revise this one on the final version.\n\n**(Line 146-147) \"To further remove class information while preserving fine-grained temporal information, we apply instance normalization,\" -> It is not straightforward to me how instance normalization can remove class information. Please clarify this.**\n\nThank you for the detailed review. The use of instance normalization (IN) in our framework aims to reduce class-specific bias by normalizing the first- and second-order statistics (mean and variance) of each instance. This technique is widely used in the style transfer literature[2], where it has been shown to effectively remove speaker ID information by suppressing global feature trends.\nIn the context of sound representation, we assume that class-related cues are embedded in global activation patterns (e.g., high energy regions specific to certain classes). IN mitigates such biases by enforcing zero-mean and unit-variance across feature maps, thereby encouraging the model to retain only temporally localized, class-agnostic details. This statistical filtering complements our overall disentanglement process in the DSDE module. We will revise the text to make this intuition more explicit.\n\nWe will clarify this in the revised manuscript.\n\n**(Line 156-157) \"To address this, we guide the static factor to retain class-relevant information by minimizing the cosine distance to the embedding vector of the n-hot class label.\" -> If I understand it correctly, this will make the static factor into class-level features rather than instance-level features, unlike the claimed \"temporal-variant\" vs \"temporal-invariant\" features.**\n\nThe static factor is explicitly encouraged to encode class-level, temporally invariant characteristics by minimizing cosine distance to a class embedding vector. This aligns with our design:\n- The static factor captures global, class-consistent features.\n- The dynamic factor models temporal, instance-specific variations.\n\n**(Table 2) How about the Waveformer architecture? Did you observe a similar trend?**\n\nThank you for your question. Due to computational resource constraints, we were unable to conduct a full ablation study on the Waveformer architecture within the scope of the main paper.\nHowever, we agree that analyzing whether the same trend holds for Waveformer is important. We are currently conducting additional experiments and will include the results of ablation study for the Waveformer in the supplementary material.\n\n**(Line 360-361) \"DRL contribute significantly to improving performance over the baseline.\" -> Please weaken this claim as there's no significance test**\n\nThank you for pointing this out. We will revise the manuscript to weaken the phrasing—e.g., replacing “contribute significantly” with “consistently improve”—to more accurately reflect the experimental evidence.\n\n**Discussion on the limitations is missing.**\n\nWe included our limitations in the Conclusion section."}, {"review_id": "koZUtGoA7J", "reviewer": "Reviewer_K1JL", "summary": "This paper presents a novel framework, Disentangled Codec Knowledge Distillation (DCKD), to improve Target Sound Extraction (TSE). Recent methods use Privileged Knowledge Distillation (PKD), where a \"teacher\" model trained with extra information (PI) guides a \"student\" model. However, existing PKD approaches often suffer from the teacher model overfitting to overly rich PI, leading to ineffective knowledge transfer. DCKD addresses this by regulating the PI's amount and flow. It first uses a neural audio codec to create a compressed representation of the target sound. It then employs Disentangled Representation Learning (DRL) to separate this representation into a static (class-related) factor and a dynamic (class-independent temporal) factor. The teacher model is conditioned in a coarse-to-fine manner: it receives general class information in early layers and the disentangled temporal PI in later layers. This prevents overfitting and facilitates effective feature-level knowledge distillation to the student model. Experimental results show that DCKD consistently improves separation performance across different TSE model architectures, demonstrating that regulating and disentangling privileged information enables more effective knowledge transfer and better student model performance.", "questions": "My questions are listed in the weakness part.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**[Strengths]**\n1. This work proposes a novel method for regulating privileged information using a neural codec for compression and disentangled learning to isolate class-independent temporal details for more effective knowledge transfer, which mitigates the teacher overfitting issue and ensures better student model generalization.\n2. The method's effectiveness is empirically validated across multiple distinct model architectures, demonstrating its robustness and general applicability for improving various target sound extraction systems, not just a single setup.\n\n**[Weaknesses]**\n1. Clarity. The main motivation of this paper is to mitigating the teacher overfitting issue by disentangling the class-independent temporal details. If my understanding is correct, the overfitting issue of PKD is mainly resulted by the class-dependent informations (the shared features of $l$ and $x$). But currently, the text in Section 1 and 3 does not clearly describe this point, which may raise difficulties in understanding the motivation of this paper quickly. For example, when I read \n2. Some out-date modules. Some modules in Figure 2 (a) adopts out-date modules like LSTM and TCN. A minor suggestion is that the authors may try to compare current backbones with Transformer based architecture for the Disentangled Codec and Diffusion Transformer based architecture for the teacher and student model.\n3. The baseline selection issue. The authors compare their work with baselines like SoundSelector and WaveFormer in Section 4.3. I agree that this experiment demonstrate the effectiveness of the proposed DCKD method. However, the comparison beyond the proposed framework is missing. For example, the authors could add comparisons with baselines based on other frameworks like SoloAudio [1], AudioSep [2]  (based on Language-oriented TSE), which would make this work more convincing.\n4. Since the concept of global and local features, mutual information-based disentanglement, privilege knowledge distillation is widely used in the speech fields, the main contribution of this work are identifying and  solving the teacher overfitting issue. \n\n\n**Reference:**  \n[1] SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer, Helin Wang et al.  \n[2] Separate Anything You Describe, Xubo Liu et al.", "was_revised": true, "final_justification": "Thanks for the response, which resolves:\n\nClarity issues.\nBackbone and baselines selection.\nUnresolved concers:\n\nThe contribution issue, Since the concept of global and local features, mutual information-based disentanglement, privilege knowledge distillation is widely used in the speech fields, the main contribution of this work are identifying and solving the teacher overfitting issue.\nConsidering current response, I will adjust my scores to borderline accept.", "rebuttal": "# Response to Reviewer K1JL\n\nWe appreciate your thorough and helpful comments. Below are our responses.\n\n**Clarity. The overfitting issue of PKD is mainly resulted by the class-dependent informations (the shared features of  and ). But currently, the text in Section 1 and 3 does not clearly describe this point, which may raise difficulties in understanding the motivation of this paper quickly.**\n \nThank you for your insightful comment.\n\nWhat we intended to convey in Sections 1 and 3 is as follows. As mentioned in lines 35–36 of the main paper (\"This issue arises because highly predictive PI can lead the teacher to overfit to training signals, producing high-variance outputs that impair the generalization ability of the student.\"), the overfitting issue in PKD arises when overly rich information is given to the teacher model all at once, without any regulation. In such cases, the teacher can overfit to the privileged information (PI), making it difficult to extract meaningful, generalizable supervision for the student.\n\nIn our framework, the overly rich PI corresponds to the target sound itself, which is oracle answer directly. To mitigate this, we compress the target audio using a neural audio codec to retain only the most essential information. Additionally, we disentangle the class information—which is already available in the TSE framework—as a static factor and inject remained dynamic factor into the later part of the teacher model. This way, we separate the information into non-overlapping components (class ID vs temporal), which are delivered progressively and in a regulated manner, reducing the risk of overfitting.\n\nWhile our disentanglement process involves separate class-dependent information from the codec representations, the core issue is not simply the presence of class-dependent features, but rather the unregulated delivery of overly rich PI to the teacher. The goal of our design is to regularize the teacher’s access to PI in both content and timing.\n\nWe appreciate your observation and agree that the current description in Sections 1 and 3 does not fully clarify this point. We will revise the text in the final version to better explain this motivation.\n\n**Some out-date modules. Some modules in Figure 2 (a) adopts out-date modules like LSTM and TCN. A minor suggestion is that the authors may try to compare current backbones with Transformer based architecture for the Disentangled Codec and Diffusion Transformer based architecture for the teacher and student model.**\n\nThank you for your helpful suggestion.\n\nWe used LSTM and TCN modules in our framework because they are well-established and widely adopted in prior work on representation disentanglement (e.g., C-DSVAE [1]) and TSE (e.g., SoundSelector [2]). Our goal was to evaluate the effectiveness of the proposed PKD scheme in a controlled setting.\n\nBy demonstrating consistent improvements with conventional backbones, we show that our method is robust and not tied to a specific model architecture. However, we fully agree that transformer-based architectures—such as Diffusion Transformers or TokenMixers—offer great potential. \n\n>[1] J. Bai, W. Wang, and C. P. Gomes, “Contrastively disentangled sequential variational autoencoder,” in Proc. Adv. Neural Inf. Process. Syst., vol. 34, 2021, pp. 10 105–10 118.\n\n>[2] T. Ochiai, M. Delcroix, Y. Koizumi, H. Ito, K. Kinoshita, and S. Araki, “Listen to what you want: Neural network-based universal sound selector,” in Proc. Interspeech, 2020, pp. 1441–1445.\n\n**The comparison beyond the proposed framework is missing. For example, the authors could add comparisons with baselines based on other frameworks like SoloAudio [1], AudioSep [2] (based on Language-oriented TSE), which would make this work more convincing.**\n\nThank you for the suggestion regarding baseline comparisons.\nOur current baselines (SoundSelector and Waveformer) were selected for their widespread adoption, and compatibility with knowledge distillation. They also represent practical scenarios where lightweight or low-resource deployment is a key consideration.\nWe are currently conducting additional experiments applying DCKD to the AudioSep framework. The results will be included in the revised version of the manuscript.\n\n**Since the concept of global and local features, mutual information-based disentanglement, privilege knowledge distillation is widely used in the speech fields, the main contribution of this work are identifying and solving the teacher overfitting issue.**\n\nThank you for highlighting this point. we will include this point in the revised manuscript."}]

Author rebuttal:

Meta-review: Summary:

The paper proposes a method for target sound extraction in a privileged knowledge distillation (PKD) framework. The main idea is to regulate the amount of target information provided to the teacher through a compressed representation of the target sound and static/dynamic (or coarse/fine) information disentanglement. The experimental results are convincing and there are a number of insightful ablations, mentioned by the reviewers.

Strengths:

- Well-motivated topic, well-written paper, well-organized results.
- Extension of PKD to example-based general sound separation.
- Consistent gains across the board.
- Nice ablations.

Weaknesses:

- Inefficient framework.
- Unrealistic assumption about having a totally clean audio as query, thus "forcing" training/results on synthetic data (but this is more of a task setup than a problem of the specific approach).
- Potential lack of generality, which the authors address with an additional experiment on ESC-50.

Reasons for accept/reject:

All reviewers agree this submission is of enough quality and that the approach could be interesting/relevant to researchers in the field. The authors also highlight the potential application of the developed concepts to related fields such as target speaker extraction, audio-visual separation, and cross-modal generation/retrieval.

Discussions:

The authors did a good rebuttal, although they already had received quite good scores at the beginning. I'd urge the authors to include the additional ESC-50 results in the main paper.
Decision: Accept (poster)