Listenable Maps for Zero-Shot Audio Classifiers
Authors: Francesco Paissan
Luca Della Libera
Mirco Ravanelli
Cem Subakan
Venue: NeurIPS 2024 poster (2024)
Keywords: Zero shot audio classifiers
Posthoc explanations
Primary area: interpretability_and_explainability
TL;DR: We propose a posthoc explanation method for zero shot audio classifiers.

Abstract: Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Zero-Shot Audio Classifiers), which, to the best of our knowledge, is the first decoder-based post-hoc explanation method for explaining the decisions of zero-shot audio classifiers. The proposed method utilizes a novel loss function that aims to closely reproduce the original similarity patterns between text-and-audio pairs in the generated explanations. We provide an extensive evaluation using the Contrastive Language-Audio Pretraining (CLAP) model to showcase that our interpreter remains faithful to the decisions in a zero-shot classification context. Moreover, we qualitatively show that our method produces meaningful explanations that correlate well with different text prompts.

Listenable Maps for Zero-Shot Audio Classifiers
Francesco Paissan" 1,2,5 Luca Della Libera Mirco Ravanelli Cem Subakan 2.3,4 Fondaziooe Bruno Kessler, "Mila, Québec Al Institute, CCncordiaa University, *Laval University. University of Trento
Abstract
Interpreting the decisions deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Zero-Shot Audio Classifiers), which, to the best of our knowledge, is the first decoder-based post-hoc explanation method for explaining the decisions of zero-shot audio classifiers. The proposed method utilizes novel loss function that aims to closely reproduce the original similarity patterns between text-and-audia pairs in the generated explanations. We provide an extensive evaluation using the Contrastive Language- Audio Pretraining (CLAP) model to showcase that our interpreter remains faithful to the decisions zero-shot classification context. Moreover, qualitatively show that method produces meaningful explanations that correlate well with different text prompts.
Introduction
The widespread adoption of AI in critical decision- making processes makes interpreting the decisions of deep learning models crucial for ensuring transparency and trustworthiness, Recently, significant research has been devoted explainable machine learning []. These efforts aim to either employ interpretable models or explain the decisions of black-box models using posthoc explanation methods. In the audio domain, however, only few works exist on interpretable audio classifiers 2 3
as well posthoc explanation methods (5) 6 7 8 The latter contributions are limited to standard closed-set classification and do not explore the challenging topic of interpreting zero-shot the other hand, are gaining popularity for their exceptional
classifiers. Zero-shot classifiers, udaptability, as they define audio classes based on a set of textual prompts necessarily predefined but can be generated dynamically during inference via natural language. The increased ficxibility of zero-shot classifiers comes with a drawback: their predictions are challenging to interpret. This difficulty arises from their multi-moda nature, learning interpreter in the joint representation space between text and audio is required. A notable example of zero-shot classifier Contrastive Language Audio Pretraining (CLAP) (10), which jointly trains audio and text representations using contrastive learning, that we also work with in this paper.
9 The class labels are not
This paper addresses the problem of posthoc explanations for zero-shot audio classifiers. To the best of our knowledge, this has never been attempted before the literature. Following the masking idea proposed in [8]. we propose LMAC-ZS (Listenable Maps for Audio Classifiers in the Zero- Shot context), which consists of a decoder (the interpreter) that outputs a saliency map capable of highlighting the regions within the input audio that trigger the zero-shot classification. We introduce a novel loss function that incentives faithfully following the similarity hetween the original audio and the corresponding text prompt. Our method provides listenable explanations for linear and non-linear frequency- short-time Fourier transform (STFT) representations of audio waveforms. can also operate the raw audio domain directly. We applied our explanation method top of a pretrained version of the popular CLAP 10 by considering different zero-shot classification datasets, including
"Correspondance to fpaissan@fbk.cm
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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f1
1 02
This is sound
4 Orest
INPUT TEXT
Text Encoder
fg
91
8/ 92
2/ as
This 18 a DOG sound
Text Encoder
Es
fg
This BIRD sound
A4
0a
Audio Encuder
Audio Encoder
Figure 1: (left) The training of the CLAP model for learning cross-modal representations. (right) Zero-shot classification with the CLAP model.
the ESC50 [1, UrbanSound8K [2]. as well as versions of ESC50 and UrbanSound8K where different types of contaminations applied. We show extensive experimental results suggesting that the produced saliency maps correlate well with the corresponding text prompts and faithfully follow the original zero-shot classifier. In particular, our evaluation using various faithfulness metrics highlights that LMAC-ZS able to provide explanations that are highly relevant to the decisions made by the CLAP model in the zero-shot context. Our method significantly outperforms traditional approaches such as GradCAM+ [13] highlighting their inefficiency in challenging tasks such as zero-shot audio classification.
In summary, our contributions are the following:
We propose a new method, LMAC-ZS, to explain zero-shot audio classifiers.
We show that LMAC-ZS maintains faithfulness the CLAP predictions across diverse zero-shot scenarios.
We qualitatively show that LMAC-ZS produces meaningful explanations for different text prompts.
1.1
Related Work
Posthoc explanation methods aim to explain the decisions of pretrained neural networks. Several works exist on producing posthoc explanations with gradient- based approaches in the computer vision literature. These include the standard saliency method [14), GradCAM [15), GradCAM+
13, SmoothGrad [16). Integrated Gradients (IG) [77. and several others. However, suggested in
18 these methods often fail to follow the classifier very faithfully and tend to be insensitive even to random model weights. Another category of post-hoc explanation methods in computer vision generates explanations by applying masks to the input data. Key approaches in this category include [19 20 21 22), which use optimization-bal -based techniques to learn and generate these masks. There also exists a series of works that are most closely related to this paper, where a decoder trained to produce explanations. Notable attempts in this vein include Dabkowski and Gal (2017) 23 Fan et al. (2017) 24], ZoIna al. (2020) 25 and Phang et al. (2020).
In the audio domain, several post-hoc explanation methods exist. These methods employ various techniques such as layer-wise relevance propagation [26]. guided backpropagation 27 and LIME
More recent posthoc explanation methods that use decoder to produce masks on spectrograms include Listen-to-Interpree (5), which uses a Non-Negative Matrix Factorization [i] based decoder to produce non-negative saliency maps. Other examples include Posthoc Interpretation via Quantization 77). which trains a VQ-VAE -based decoder as an explanation module, and Listenable Maps for Audio Classifiers [ which trains decoder using classification loss to promote faithfulness. These works are not directly applicable to zero-shot classification as they require predefined set of labels to train the interpreter. In this paper, our goal is to produce explanations in a true zero-shot fashion. To achieve that, we train our decoder on the same data as the CLAP model (without using class labels that will later test on). Subsequently, LMAC-ZS can produce explanations for arbitrary labels, encoded as natural language. This includes labels not previously seen during the training of the interpreter.
28
29
30
2 Preliminaries
In this Section, we first present the learning methodology for audio-text cross-modal representations in Section 2.1 Then, we introduce masking-basee posthoc explanations in Section 2.2

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Listenable
Interp.
Lz (0)
INPUT TEXT
Smail))
t
IIVIEM Npnce
X;
InpurTY
(-)
h4
Mo(..))
M
InputTí
fandio(.)
Figure 2: LMAC-ZS architecture. The input spectrogram (linear frequency) X; (the i-th audio the batch) first of all passes through the transformations (InputTff block) make it compatible with the input domain (e.g. Mel Spectra) of the audio encoder faudio(),, which yields the latent representations h4- These representations along with the text representation t, (the j-th text prompt within the batch) are then fed to the decoder Mo(-.-). The resulting mask is then element-wiss wiie multiplied with the input spectrogram X;, The masked spectrogram M @ X; is then converted back to the input domain of the audio encoder, and the similarity score !! I /mndio (Mo(4,, h4) @ Xandia.) is calculated, which used the overall training objective Lzs(0) The listenable explanation produced by simply inverting the masked spectrogram through the inverse-STFT by incorporating the phase spectrogram of the input Xphase-
2.1 Contrastive Learning of Audio-Text Cross-Modal Representations
The goal of learning audio-text cross-modal representations is to create a joint latent space between text and audio. CLAP (Contrastive Language-Audis Pretraining) [10), achieves this via contrastive learning. That is, the similarity between the latent representations of a text and audio signal is maximized if they form a pair, otherwise this similarity is minimized. More specifically, consider X, and X, as batches of text and audio data, respectively. Within the CLAP model, the latent representation is derived by passing the text and audio through their respective encoders, denoted as (.) and (.). This process produces the text and audio latent representations, denoted as Luga Iwer (uut) and Laudio Jaudio (Xaudio). respectively. Here, Lusa is matrix of dimensions R R where is the batch size and T represents the latent dimensionality of text. Similarly, Laudio matrix of dimensions IR. NAA, where A denotes the latent dimensionality of audio. CLAP trains joint latent space by passing Luea and Laudio through fully-connected layers such that,
t= MLP MMLPixx(LLe (oxaL))))) a MLP audioo Laudio);
where M.P(.) denotes the multi layer perceptron transformation layers. The matrix t E N and E TR- respectively denote the text and audio latent variables with the same latent dimensionality d. As a shorthand for the rest of the paper we will denote the combination of encoders and the MIL with fiea(.) MLP (9tenn()))) and f.)dio() ;= MLP audio (g for text and audio, respectively. The model aims to maximize the diagonal entries on the matrix C' = ta The matrix C E R Nx represents audio-text pairings. The diagonal elements Ct,; correspond to positive samples, while other elements are negative samples. This translates into the following training loss function:
1 [(C)
Σ log Softmax, (C/T)i, log Softmax,(C/r)))
2
where Softmax;(.) and Softmax, respectively denote Softmax functions along text and audio dimensions, is temperature scaling parameter, and the Ct2 denotes the diagonal elements of the C' matrix. We show the training forward pass pipeline in the left panel of Figure T
We would like to note that with this framework, the zero-shot classification amounts to calculating the similarity of the representation a class labels. Namely, the classification decision is taken as:
given audio with set of text prompts, each corresponding to
G arg max 1.J Ouest arg max feeu (prompt;)) faudio (Xtest andhoo:
where & is the zero-shot classification decision, Grest is the embedding for the test audio, and Lj is the text embedding corresponding to the label of class j (represented via prompt,). We show the pipeline of zero-shot classification in the right panel of Figure
3

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2.2 Saliency Maps For Fixed Set Audio Classifiers
In this work, we adopt posthoc explanation method that uses learnable decoder, following the masking idea introduced in L-MAC [8. Before we delve into how to generate a saliency map for zero-shot classifier, we first explain how L--AA produces a saliency map within the context of a standard classification setup. The loss function that is minimized during training in L-MAC to obtain faithful saliency maps is defined as follows:
(() -CrossEnt(i; (Mo(h) o X)) CrossEEn(s. ((1 Ma(h)) o X)) (l) (4)
The first term in this loss function aims maximally align the classifier prediction arg max. fc(X), with the classifier output obtained after masking the input, i.c. the logit (Mo(h) o X) ReNe, where Ne is the number of classes. Note that CrossEnt(.) denotes the CrossEntropy loss function. The decoder network Ma(h) takes in the classifier representations h (which can consist of representations of several layers) and produces mask (with values within the interval [0. 1] and same size as the input) that is element-wiss multiplied with the input X. A regularization term that consists of an L1 loss is also used prevent trivial solutions, such mask with all values set to 1. The mask-out term -CrossEnt(2 (g. ((1 Mo)h) @ X)) minimizes the relevance of the mask-out portion to the predicted class T7 the next section, we introduce our framework for explaining zero-shot classifiers (that we have defined in Section 2.1 which again applies a mask the input to replicate the original text-audio similarities.
Saliency Maps for Zero-Shot Audio Classifiers
Similarly to the method introduced in L-MAC [ and summarized in Section 2.2 our goal is to generate explanations that faithfully follow the model. However, in the context of zero-shot classifiers, we do not have model that outputs a fixed number of logits. Hence, we need different loss function that promotes faithfulness between the explanations and the zero-shot audio classifier, which relies on similarities to make its decisions. We denote the similarity between the i-th text prompt and j-th audio recording with Ci. as,
=t= fadio (Xadioo
faudio
(5)
Our methodology based on obtaining saliency map such that the text-audio cross-modal similarity matrix C' is maximally preserved after masking the important parts of the spectrogram. In other words, we learn a decoder such that, after masking the audio, the similarity with text prompts within the batch is maximally preserved. To this end, we define the loss function as follows:
Cr)(0) = [
Ca. - faudia (Mo(ti, h2) 2 Xandinn) ) + > Mo ba)
i.j
(6)
+ A2 E a(xaudio.)..
Here, the first term aims to minimize the discrepancy between the original similarities Ci,y and the similarities after masking the input audio Xandio, RTxF using the decoder Mo(t, by). which outputs a mask of shape T x F. Importantly, the decoder is conditioned on both the text representation = fieu (X (Xwa,s) that corresponds the i-th text prompt in the batch, and the representations hj- which includes the last 4 representations obtained from the audio encoder faudio (xadio.)). ). A1, 22 are tradeoff parameters.
The second term Equation promotes sparsity the generated mask to avoid trivial solutions. Finally, the last term D.)) aims to increase the diversity of masks generated for a given audio when conditioned on different text prompts. is defined as:
(XXaddio::)
Σ ji
faudio (Xmutio,s O Mo(t.,ha)) fantto (Xandio, @ Molt,, hi)
(7)
The goal of this term is to align the uni-modal similarity between text embeddings t,, ty with the uni-modal similarity between the corresponding audio embeddings faudio (Xaudio, O My Mo h,)).
fandio (Xudio,s @ Mo((t,,h)) obtained from the corresponding masked spectrograms. The intuition

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is that the similarity between two text prompts should be reflected in the similarity of the audio embeddings from the corresponding masked spectrograms: the farther the text prompts, the farther apart should be the corresponding audio embeddings from masked spectrograms, and thus, the more different the masks should be. We show the effectiveness of this term on diversity with respect to different text prompts in Section[B] The overall pipeline is shown in Figure
Producing Listenable Explanations: Our method employs its masking in the linear Short-Time Fourier Transform (STFT) domain, and therefore generating listenable explanations through the inverse-STFT is possible. The listenable explanation is obtained through the following operation,
Tint ISTFT ((X @ M)eeX
(8)
where both the explanation mask M and the input audio X are in the linear-scale STFT domain, and X phase is the phase of the original input audio. This operation is also shown in Figure 2
4
Experiments
4.1 Metrics
To evaluate our method, we employ faithfulness metrics previously used in the audio interpretabillity literature for standard classification setups. We adapt such metrics to the zero-shot scenario by using the class prediction probabilities defined by audio-text text similarities such that
expll] Crest
p(c j) No.
Exe exp(t) Grest)
where p(c = is the probability of predicting the class that corresponds to the j-th text prompt and No is the total number of text prompts used in the zero-shot setting. Analogously CLAP 10 to create prompts that correspond to the predefined classes in ESC50 [1]] and UrbanSound8K
augment the class labels with the prefix "this is the sound of",, obtaining prompts such as "his is the sound of baby crying", "this is the sound of cat". When computing all the metrics for LMAC-ZS, we conditioned the decoder on the text prompt that corresponds to the model prediction
C = arg maxt, tj] Agsst
Faithfulness Spectra (FF); Introduced in [5] it assesses the importance of the provided ex- planation for the classifier. The metric calculated by measuring how much does a class-specific prediction probability drops after removing the explanation signal from the original. It is defined as
FF pa (X..) pa( X, - Xint),
where 2 is the class prediction given by the elassifier. High faithfulness values mean that the masked- in portion of the input spectrogram X is highly influential for the classifier decision of the predicted class こ We report the average faithfulness over all examples by reporting the average quantity
FF >,, } FF,, Larger is better.
Average Increase (Al): Introduced in [13, it measures the increase in confidence for the masked-in portion of the explanation, and it calculated as follows:
AI Σ (pa(X, @ M) > pa(X,)) 100,
where [.], is the indicator function, which is one if the argument is true, and zero otherwise. For this metric, larger is better.
Average Drop (AD): Introduced in 13 it measures the decrease in model confidence when the input image is masked, and it is calculated as follows:
N max(0,,p(X.) Pa((nn C M)) 100.
AD
pal (x,)
For this metric, smaller is better.
5

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LMAC
GrodCame
Model Ron-ooitttion Tee SSIM
0.7
0.5
0.0
Mad pean
0.5
0.4
0.4-
0.3
0.3
0.4
-laaCCaa ESCS4
6.1
VIACUS USEC
0.0
GruOO CO USSX
-0.2
0.0
0.2
0.4
0.5
0.8
0.22
0.0
0.2
0.4
0.
0.5
Similarity
Similarit
ClovBlcck IT
Figure 3: (left) Mask-Mean vs Similarity for LMAC-ZS, (middle) Mask-Mean vs Similarity for GradCam++, (right) Model Randomization Test for LMAC-ZS and GradCam++.
Average Gain (AG): Introduced 33 it measures the increase in confidence after masking the input image. It is calculated follows (larger is better):
1 N max(0, max(0.pa(X, O M) Pa--pa(K (x,)) 100.
AG
N
1 --aaXXn))
Input Fidelity (Fid-In): Introduced in 77. measures whether the classifier outputs the same class prediction on the masked-in portion of the input image. It is defined as the following and the larger is better,
Fn
N Σ aag max (x(X,) arg max per (X, @ M)].
Sparseness (SPS): Introduced in 34, it measures whether only values with large predicted saliency contribute to the prediction of the neural network. Larger values indicate more sparse/concise saliency maps. We use the implementation from the Quantus library [35.
Complexity (COMP): Introduced in (36,, it measures the entropy of the distribution of contributions from each feature to the attribution. Smaller values indicate less complex explanations. We again use the implementation from the Quantus library.
4.2 Experimental Setup
We use the official pretrained CLAP 10 weight to perform zero-shot classification on ESC50 II| and UrbanSound8K [12 datasets. We train LMAC-ZS on the datasets on which CLAP had been trained (namely, Clotho 37 FSD50K (38, AudioCaps [39], and MACS 40 which are publicly available). We also explored training LMAC-ZS only on Clotho to simulate the case where the computational budget is limited. The models were trained on a single NVIDIA RTX 3090 GPU. For the LMAC--S model that is trained on the Clotho dataset, we did 2 epochs on the complete dataset, for which an epoch approximately takes an hour. For the Full CLAP data we did epochs as well, and an epoch takes around 4 hours. We quantitatively test whether LMAC-ZS follows the zero-shot classifier on In-Domain (ID) and Out-of-Domain (OOD) settings. For the In-Domain setting, we perform zero-shot classification clean audio from ESC50 and UrbanSound8k and then produce explanations for the classifications using LMAC-ZS. We would like to emphasize that LMAC-ZS has only been trained on the training datasets for CLAP, and has not been fine-tuned on ESC50 or UrbanSound. For the Out-of-Domain setting, we contaminate the audio with various noise sources at 3dB Signal-to-Noies Ratio (other audio from the same dataset, woise- and human speech from the LJ-Speech [4I] datasct).
We explore masking in the Mel-domain explore the case where we produce explanations directly the feature space on which CLAP operates. For Mel-domain we used 44. 1kHz data on which the CLAP model is trained. We also explore masking in the linear frequency-scal log PTF
httpps//eenodoorg//eeert///////

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Table 1: In-Domain quantitative evaluation for the ESC50 and UrbanSound8K Datasets. Two versions of LMAC-ZS are compared: (CT) trained on the Clotho dataset only and (Full) trained on all CLAP datasets. MM denotes the Bask, the average value for the obtained masks.
Metric
(1)
AD (1)
AG
FF (t)
Fid-In (1)
SPS (1)
COMP ())
MM
7S classification on ESC50,
Mel-Masking, 80.7% accuracy
Gradcam
2.90
45.85
1.01
01.28
0.19
0.71
9.52
0.15
GradCam++
8.45
35.07
3.19
0.50
0.39
0,41
10.32
0.35
SmoothGrad
0.50
52.76
0.12
0.024
0.036
0.301
10.52
0.039
IG
0.25
53.47
0.054
0.064
0.022
0.57
10.09
0.037
LMAC-ZS (CT)
29.00
12.25
12.93
0.49
0.80
0.78
9.40
0.14
LMAC-ZS (Full)
23.45
17.12
10.31
0.51
0.68
0,80
9.12
0.17
7S classification on ESC50, STFT- ITFT-Masking. 78. 78.9% aсcuraсу
GradCam
20,30
23.75
7.77
0.78
0.58
0.72
11,54
0.14
GradCam++
32.50
8.97
7.95
0.79
0.84
0.41
12.41
0.35
SmoothGrad
6.95
32.75
2.85
0.78
0.47
0.53
11.98
0.0001
IG
16.10
21.51
6.05
0.79
0.65
0.74
11.58
0.0095
LMAC-ZS (CT)
37.40
7.43
11.26
0..88
0.86
0.50
12,29
0.11
LMAC-ZS (Full)
43.35
4.29
10.57
0.78
0.90
0.65
11,86
0.1
ZS classification on US8K, Mel Mel-Masking,
71.7% accuracy
GradCam
2.34
47.55
1.09
0.26
0.16
0.78
9.32
0.12
GradCam++
7.21
33.4
3.33
0.56
0.44
0.41
10.27
0.39
SmoothGrad
1.21
49.68
0.43
0.04
0.11
0.33
10.49
0.04
0.98
50.77
0.35
0.15
0.09
0.60
10.02
0.03
LMAC-ZS (CT)
23.41
20.58
12,88
0.51
0.65
0.85
9.01
0.08
LMAC-ZS (Full)
35.69
15.65
18.19
0.48
0.72
0.79
8.95
0.17
ZS classification
US8K,
STFT-Masking 68, 9% accuracy
GradCam
18.67
26.1
11.18
0.79
0.53
0.77
11.41
0.12
GradCam++
32.85
8.84
13.16
0.81
0.83
0.41
12.34
0.39
SmoothGrad
15.31
23.56
7.67
0.81
0.61
0.54
11.97
0.0001
IG
22.65
19.53
12.31
0.77
0.66
0.79
11.36
0.01
LMAC-ZS (CT)
32.71
14.57
14.69
0.75
0.72
0.55
12.12
0.08
LMAC-ZS (Full)
40.85
7.79
15.52
0.78
0.85
0.76
11.34
0.07
domain be able provide listenable explanations. For STFT domain filtering worked with 16kHz data. We would like to note that this results in slight changes in zero-shot classification We trained LMAC-ZS with batch size of 2 using the Adam optimizer 42] with a learning rate of Tc 1c-5. The decoder consists of a series of transposed conditioning by using cross-atee coss-attention similar to that used in Stable Diffusion [4 The implementation is done
accuracies, which reported the Tables I
convolutions upsample from CNN14 43 CLAP representations and incorporates
using the SpeechBrain toolkit 45 46
and it can be accessed through
Quantitative Comparison
We compare LMAC-ZS with popular gradient-basee saliency map methods including GradCam [1)..
GradCam++ 13). SmoothGrad [16), and Integrated Gradients (IG) [77.. We apply these saliency map methods using only the CNN14 audio representations. The class logit with respect to which the class activation map for these methods is calculated is picked by using the zero-shot classification decision arg max; 1.] Osest-
In Table I we compare the faithfulness of the explanations obtained on In-Domain data, where we performed zero-shot classification on clean ESC50 and USSk recordings. We observe that on ESC50 with Mel-Domain masking, LMAC-ZS obtains better AI, AD, AG, FF, and Fid-In values. We observe similar trend for AI, AD, and AG with STFT-domain masking also, while FF values are comparable. On the UrbanSound8K dataset, we also observe that in terms of AI, AD, and AG the best results are obtained with LMAC-ZS trained with the Full CLAP training datasets. In terms of mask sparseness (SPS) and Complexity (COMP) in most cases, the best results are obtained with the proposed model.
In Table 2 compare the faithfulness of the explanations obtained on ESC50 samples contaminated with three different types of background noises. We observe that with Mel-Masking, LMAC-ZS
https://trancescopiisssaa

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Table Out-of-Domant Domain quantitative evaluation for the ESC50 Dataset.
Metric
(t)
(D
AG (1)
FF (*)
Fid-In (1)
SPS (f)
COMP (L)
MM
ZS classficction on ESC50,
Mel-Masking, ESCS0 contamination, 57.2% aceuracy
GradCam
6.78
40.71
3.13
0.29
0.19
0.69
9.66
0.18
GradCam++
9.82
35.81
4.53
0.42
0.29
0.39
10,40
0.35
SmoothGrad
0.62
48.55
0.13
0.024
0.022
0.29
10.54
0.039
IG
0.55
48.88
0.091
0.073
0.020
0.56
10.13
0.039
LMAC-ZS (CT)
19.25
24.30
8.83
0,40
0.49
0.81
9.18
0.13
LMAC-ZS (Full)
20.43
21.57
9.71
0.42
0.54
0.82
9.08
0.15
ZS classification
ESC50,
STFT ESC50 contumination, 58, 6% accuracy
GradCam
23.77
25.25
12.24
0.69
0.49
0.69
11.73
0.17
GradCam++
29.52
14.84
10.17
0.70
0.70
0.39
12,48
0.35
SmoothGrad
11.80
30.63
5.15
0.70
0.42
0.52
12.06
0,0002
IG
16.37
25.67
7.21
0.70
0.51
0.71
11.73
0.011
LMAC-ZS (CT)
35.65
12.23
13.04
0.69
0.74
0.53
12.18
0.09
LMAC-ZS (Full)
39.4
8.28
11,81
0.69
0.80
0.67
11.79
0.09
ZS classificution on ESC50,
Mel-Masking. White Noise contamination, 65.2 accuracy
GradCam
3.65
43.79
1.43
0.34
0.12
0.75
9.41
0.14
GradCam++
7.12
37.03
2.97
0.52
0.26
0.43
10.33
0.335
SmoothGrad
1.72
47.93
0.56
0.040
0.040
0.28
10.54
0.035
IG
1.57
47.97
0.55
0.084
0.039
10.16
0,034
LMAC-ZS (CT)
28.52
12.78
0.64
LMAC-ZS (Full)
14.25
27.92
6.62
0.41
0.42
0.86
8.86
0.11
ZS classification on ESC50, S7FT-Masking, White Noise contamination, 57.4% accuracy
GradCam
14.92
31.89
5.95
0.32
0.77
11.40
GradCam++
19.50
24.01
0.50
SmoothGrad
7.10
36.53
2.66
0.66
0.25
0.52
12.15
0.0004
IG
10.17
34.35
4.89
0.66
0.30
0.69
11.80
0.011
LMAC-ZS (CT)
19.85
21.51
7.13
0.63
0.53
0.52
12.24
0.08
LMAC-ZS (Full)
32.97
11.86
10.63
0.64
0.70
0.65
11.85
0.09
ZS classification on ESC50, Mel-Masking, LJ-Speech contamination, 64.8% acсumиcy
GradCam
6.50
39.05
3.06
0.33
0.20
0.70
9,66
0.18
GradCam++
12.85
32.81
6.50
0.47
0.32
0.41
10.36
0.35
SmoothGrad
0.63
47.40
0.17
0.03
0.02
0.28
10.55
0.04
IG
0.53
47.70
0.10
0.10
0.01
0.56
10.12
0.04
LMAC-ZS (CT)
24.38
20.69
11.29
0.43
0,56
0.80
9.26
0.11
LMAC-ZS (Full)
8.95
30.55
3.69
0.38
0.35
0.86
8.79
0.10
ZS classification
ESC50,
STFT-Masking, L/-Speech contamination, 64% accuracy
GradCam
24.93
22.91
12.78
0.67
0.50
0.70
11.72
0.18
GradCam++
34.13
12.24
10.84
0.67
0.72
0.41
12.44
0.34
SmoothGrud
9.18
29.60
3.91
0.67
0.40
0.53
12.05
0.00
IG
15.55
27.15
6.51
0.66
0.46
0.73
11.67
0.01
LMAC-ZS (CT)
25.77
17.79
9.67
0.63
11.96
LMAC-ZS (Full)
25.73
7.23
0.62
0.72
11.47
reaches better performance terms of AI, AD, AG, and very comparable numbers in terms of Fid-In We also observe that in terms of Sparsity and Complexity LMAC-ZS yields better masks in the LJI-Seechh contamination, we observe that LMAC-ZS
Mel Domain. In the STFT domain except obtains better performance in terms of AI, AD, and AG. We would like to note that GradCAM++ obtains better FF numbers in general, but we note that GradCAM+ mask areas are larger as shown in the last column with MM. We also observe similar trends for the explanations obtained on US8K samples contaminated with various background noises shown in Table Another point to note is that in general LMAC-ZS trained on the full CLAP training set yields better performance. However, observe that training LMAC-ZS -ZS only on the Clotho dataset yields to comparable or better performance (e.g. ESC50, Mel, white noise contamination). This shows that, in situations where there is limited access to computational resources, training only on Clotho can produce faithful explanations. We furthermore compare the effect of changing the size of the training size for the interpreter in Appendix
4.4 Qualitative Comparison and Sanity Checks
We provide some qualitative examples of generated explanations in Figure and compare with GradCAM+ which seems to provide the most faithful explanations among the basclines according

---

Input 'Cat'
Explain 'Cat'
Explain 'Glass breaking"
312
LMAC-ZS
66
120
05
80
Input 'Cat'
Explain 'Cat' Sim=0.71
Explain 'Glass breaking" Sim=-0.03
GrdCAM++
315
Figure 4: Qualitative Comparisons of Explanations given by LMAC-ZS, and GradCAM++, for two different classes. We that LMAC-ZS shuts-off the explanation depending the similarity of the given prompt with the input audio, whereas GradCAM+ remains insensitive to the class label.
to the Tables and 3
We see that LMAC-ZS generates explanations that are much more sensitive the similarity between the prompt and the input audio. For instance in LMAC-ZS explanations we see that if there exists a large similarity between the text prompt and the input audio, the mask correctly highlights relevant portions of the input spectrogram. Also, see that if the similarity between the input and the text prompt is small then the mask tends not to highlight any areas as expected. For instance Figure 4 we see for the input recordings that corresponds to a 'Cat', both LMAC-ZS and GradCAM++ return reasonable explanations. However, when we prompt LMAC-ZS for an unrelated prompt (e.g. "Glass Breaking' this case), it correctly returns an empty explanation mask, as it is impossible to explain. On the contrary, when GradCAM+ returns a class activation map corresponding to the class "Glass Breaking," " we observe that the explanation remains unchanged.
To measure the correlation between the mask mean and similarity, Figure 3 presents a scatter plot depicting the relationship between the similarity of the input text prompt and audio. For LMAC-ZS, we observe that explanations are appropriately returned as empty (indicating small Mask-Means) when the similarity score, estimated using CLAP embeddings, low. Whereas for GradCAM++, the mask mean and similarity appear to be independent of each other.
Finally, we conduct cascading model randomization sanity check [1]] to assess the sensitivity of explanations returned by LMAC-ZS to the CLAP weights. As illustrated Figure 3 after three layers of randomization, the similarity drastically decreases for LMAC-ZS, while remains constant for GradCAM++. We visualize these explanations in Figure and provide additional samples in Appendix A.2 More qualitative samples available through companion website"
5 Limitations and Societal Impact
Our current implementation focuses on fixed-length audio for simplicity. However, the core methodology of LMAC-ZS can be extended to handle variable-length inputs. Additionally, while this work employs standard faithfulness metrics that analyze the dominant class contribution, LMAC- ZS allows for investigating contributions from the top k classes. Studying the top contributions to faithfulness could provide further insights into the model's 's decision-makiie process. Lastly, our study is limited to the CLAP model, primarily selected for its widespread adoption within the field. It worth mentioning that there is limited availability of alternatives. For instance, most alternative models such LAION CLAP [7 are still variations of CLAP, offering minimal differences in their core architecture.
Limitations:
http:://ttannessoiiisaaaLttt

---

We believe this research has the potential for societal benefits, particularly in healthcare applications. While this work does not directly target medical diagnosis. improved explainability of audio classifiers for speech pathologies could make them more trustworthy and accepted by medical professionals. We do not see direct negative societal impacts from this research.
Societal Impact:
Conclusions
This paper, to the best of our knowledge, represents the first attempt to develop model specifically designed for interpreting the decisions of pre-trained zero-shot audio classifiers. In particular, we introduce LMAC-ZS, novel post-hoc explanation method employing a specialized decoder that generates saliency maps highlighting the regions of the audio input that most contribute to the model predictions. Extensive evaluations highlighted that LMAC-ZS effectively generates explanations that closely align with the decisions made by the CLAP model in zero-shot settings. Our quantitative and qualitative comparisons show that LMAC-ZS outperforms or is comparable to the most popular baseline saliency methods most quantitative faithfulness metrics. Additionally, LMAC-ZS offers the possibility of being prompted for an explanation. This ability is missing in traditional methods and allows users gain further insights into the decision-making processes conducted by the model.
Acknowledgements
This research enabled in part by support provided by Caleul Québec and the Digital Research Alliance of Canada, and the funds provided by Natural Sciences and Engineering Research Council (NSERC) of Canada.
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A Appendix supplemental material
A.1
Results on UrbanSound8K Dataset with Contaminations
Table 3: Out-of-Domain quantitative evaluation for the UrbanSound8K Dataset.
Metric
AT
(
AG
FF (1)
Fid-In (f)
SPS (*)
COMP (J)
MM
ZS classification
US8K, Mel-Masking, US8K contamination, 57% accuracy
GradCam
2.64
48.43
1.43
0.27
0.12
0.77
9.42
0.13
GradCam++
7.58
37.89
3.91
0.56
0.33
0.37
10.39
0.40
SmoothGrad
2.16
50.12
1.14
0.05
0.08
0.32
10.51
0.04
IG
1.82
49.79
0.82
0.18
0.07
0.59
10.06
0.03
LMAC-ZS (CT)
17.74
25.57
9.87
0.48
0.55
0.86
8.95
0.07
LMAC-ZS (Full)
36.08
16.98
19.23
0.47
0.69
0.77
9.00
0.19
ZS classification on US8K, STFT-Masking, ESC50 contamination, 57% Accuracy
GradCam
17.83
31.78
12.05
0.78
0.42
0.76
11.51
GradCam++
28.81
14.56
14.42
0.78
0.73
0.37
12.48
SmoothGrad
23.13
20.58
13.73
0.79
0.64
0.52
12.12
0.0002
IG
21.53
22.41
12.76
0.74
0.60
0.77
11.53
0.01
LMAC-ZS (CT)
31.09
17.69
15.29
0.72
0.66
0.55
12.12
0.08
LMAC-ZS (Full)
39.42
11.53
17.51
0.75
0.78
0.78
11.23
0.06
7S classification
US8K,
Mel-Masking, White Noise contamination, 62% accuracy
GradCam
6.77
44.01
3.91
0.35
0.21
0.73
9,46
0.16
GradCam++
12.51
37.77
8.49
0.60
0.31
0.38
10.38
0.39
SmoothGrad
3,55
49.01
1.60
0.04
0.11
0.31
10.52
0.03
IG
2.51
48.43
0.94
0.08
0.13
0.56
10.11
0.03
LMAC-ZS (CT)
42.70
12.02
25.78
0.42
0.76
0.87
8.91
LMAC-ZS (Full)
34.53
14.13
20.32
0.39
0.80
0.88
8.72
ZS classification on US8K,
STFT-Masking, White Noise contamination, 61. 1% accuracy
GradCam
18.24
35.12
12.24
0.76
0.34
0.74
11.48
0.15
GradCam++
20.16
27.33
13.21
0.76
0.49
0.38
12.48
0.38
SmoothGrad
21.36
27.98
14.25
0.76
0.47
0.52
12.21
0.10044
IG LMAC-ZS (CT)
19.91
33.36
13.74
0.72
0,36
0.69
11.79
0.01
27.78
17.64
13.44
0.69
0.66
0.59
12.05
0.07
LMAC-ZS (Full)
46.51
9.95
25.28
0.69
0.81
0.70
11.60
0.06
7S classification on US8K, Mel-Masking, LJ--ehech contamination, 44. accuracy
GradCam
3.49
46.48
1.69
0.28
0.14
0.68
0.19
GradCam++
10.86
36.61
6.28
0.45
0.32
0.37
10.39
0.41
SmoothGrad
2.04
50.09
0.03
0.05
0.31
10.35
IG
49.80
0.74
0.12
0.05
0.60
10.03
LMAC-ZS (CT)
25.78
23.54
17.43
0.37
0.55
0.86
LMAC-ZS (Full)
36.24
13.90
20.47
0.41
0.73
0.86
8.79
ZS classification on US8K,
STFT-Masking, LJ-Speech contanination, 46. 1% accuracy
GradCam
21.48
28.71
14.13
0.76
0.45
0.69
11.74
0.19
GradCam++
38.74
11.53
17.95
0.76
0.76
0.37
12.47
0.40
SmoothGrad
34.35
19.43
24.32
0.76
0.62
0.52
12.11
0.00
IG
34.57
20.43
26.10
0.69
0.60
0.74
11.59
0.01
LMAC-ZS (CT)
35.96
15.91
18.33
0.68
0.67
0.63
11.92
0.07
LMAC-ZS (Full)
32.51
13.79
15.77
0.72
0.74
0.79
10.99
0.02
A.2
Qualitative Analysis of Model Randomization Test
Figure 5 presents qualitative visualization of Model Randomization Test results for GradCAM++ and LMAC-ZS.
A.3
Qualitative Comparison with GradCAM++
Figures 6 show additional sample for the quality of the explanations spectra.
14

---

Cascading randomization (Conv Block ID)
GradCAM+r LMAC-ZS
Figure 5: Visualization of Explanations after Cascading Model Randomization. Left column is the input, second column is the original explanation, and more we go towards the right more layers randomized. Top row is for LMAC-ZS, and the bottom row is for GradCAM++.
Explain 'Dog' Sim=0.59
Explain 'Rain" Sim=-0.10
Input 'Dog'
LMAC-ZS
Explain 'Dog' Sim=0.59
Explain 'Rain' Sim=-0.10
Input 'Dog'
GradCAM++
Figure Qualitative Comparisons
Explain "Toilet flushing' Sim=0.69
Explain "Water drups Sim=0. 14
Explain 'Jackhammer" Sim=-0.01
Input 'Toilet flushing'
LMAC-ZS
Explain 'Toilet flushing' Sim=0069
Explain 'Water drups" Sim=0. 14
Explain 'Jackhammer' Sim==0.01
Input 'Toilet flushing'
GraCCM++
Figure 7: Qualitative Comparisons 2
15

---

B Explanation sensitivity to Audio-Text Similarity
To showcase the effectiveness of the additional diversity term (Equation 7 conducted qualitative and quantitative tests to evaluate the explanation sensitivity to text prompts.
B.1 Qualitative results
We compare the explanations obtained with LMAC-ZS with and without the diversity term in Eq 7 We present the results in Figure [ and Figure | respectively. The results are obtained for the model that does masking in the STFT domain and was trained on FSD50K. We present the plots using log-frequency scaling. In each plot, we give the original text-audio similarity (in the title of the first subplot), well as the audio-text similarity after masking the audio (in the title of the third subplot). Note that the predicted class is also used as the prompt for the masking model. We observe that masks are more sensitive to text prompts because of the additional diversity term.
Inp. original cim 0.49
Ins original diri 0.23
406
250
aradicad koo - doy
Prsaiccaal clas train Domirom: oudio
dog
Auc
Time
et STFF masked sm 0.56
x STFT masked sim - 0.30
496
130
Figure 8: Explanations obtained with the additional diversity term (Eq 7
inp. original sim 0.49
ins, origin all sim 0.25
2045
Predicted dog
Fred cted class train
the
0392
5112
x STTT masked in 053
X STFT masked sim 0.2
1930
Figure 9: Explanations obtained without the additional diversity term (Eq
B.2 Quantitative results
we present the 2D histogram of mask mean and similarity between text and audio after masking. This highlights the increased mask sensitivity of the model to different text prompts when the diversity term in Equation 7 of the paper is utilized. We see in the left panel of Fig. 10 that without the diversity term, the mask means do not have discernible correlation with the text-masked audio similarity.
In Figure 10
16

---

Without diversity term
With diversity term
30
14
0.4
Mask Means
40
Mask Means
0.2
0.5
0.08
0.18
009
10
0.0
0.0
-0.2
Similarities
Similarities
Figure 10: Audio-text similarity after audio masking, without the diversity term (left), with the diversity term (right).
C Ablation on the training dataset size for LMAC-ZS
Table 4: Interpreter performance for different training dataset sizes and for additional baselines.
Metric
AI (f)
AD
AG (1)
FF (1)
Fid-In (1)
SPS
COMP (4)
MM
ZS classification on ESC50, STFT-Masking, 70.7% accuracy
ScoreCAM
29.97
12.14
8.82
0.70
0.75
0.32
12.59
0.41
GScoreCAM
29.64
8.56
6.62
0.79
0.84
0.36
12.52
0.39
LMAC-ZS Clotho
37.40
7.43
11.26
0.86
0.50
12,29
LMAC-ZS FSDSOK
34.00
8.33
10.12
0.83
11.83
LMAC-ZS AudioCaps
39.00
5.93
10.43
0.88
0.68
11.67
LMAC-ZS MACs
15.61
22.86
5.32
0.78
0.61
0.42
12.42
LMAC-ZS Subset (25%)
41,50
3.48
7.99
0.65
11.91
0.22
LMAC-ZS Subset (50%)
43.70
3.54
7.86
0.79
0.91
0.63
11.97
0.19
LMAC-ZS Subset (75%)
40.60
4.74
7.73
0.79
0.89
0.66
11.84
0.17
LMAC-ZS All Data
43.35
4.29
10.57
0.78
0.90
0.65
11.86
0.10
ZS classification on ESC50,
STFT-Maste Masking ESC500 contomination,
accuracy
ScoreCAM
31.39
7.03
7.05
0.79
0.87
0.36
12.52
0.39
GScoreCAM
28.07
13.74
8.42
0.73
0.32
12.59
LMAC-ZS Clotho
35.65
12.23
13.04
0.74
0.53
12.18
LMAC-ZS AudioCaps
35.97
10.35
11.42
0.71
11.63
LMAC-ZS FSD50K
26.95
16.26
9.97
0.67
0.65
0.66
11.59
0.03
LMAC-ZS MACS
11.38
31.54
4.42
0.68
0.38
0.44
12.41
0.05
LMAC-ZS Subset (25%)
42.65
5,99
9.81
0.70
0.84
0.66
11.90
0.20
LMAC-ZS Subset (50%)
39.47
7.52
9.05
0.71
0.81
0.66
11.88
0.16
LMAC-ZS Subset (75%)
40.42
7.07
8.84
0.70
0.83
0.68
11.80
0.16
LMAC-7S All Data
39.47
8.28
11.81
0.69
0.80
0.67
11.79
0.09
LMAC-7S is decoder-based interpreter. That is, we train the decoder based on the pre-trained classifier's representations. The amount and quality of training data can impact both the performance and the training time of the interpreter. We benchmarked our interpreter different datasets with different sizes, i.c. the datasets that are included within the whole CLAP training set (denoted with All Data in Table 4 The datasets that make up the whole CLAP training set are, Clotho (37), MACs [40), FSD50k 38 and AudioCaps [39]. We have also experimented with randomly subsampling the whole
CLAP training set and denoted it as 'Subset' in
In Table we report the interpreter performance for the aforementioned training datasets with different sizes. We note that the explanation's faithfulness is comparable when training the decoder on the entire training data or a subset; this indicates that it possible to train LMAC-SS on smaller dataset and still obtain faithful explanations.
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Table 5: Frechet Audio Distance of the training datasets with respect to ESC-50.
MACs
FSD50k
Clotho
AudioCaps
Subset (25%)
ESC-50
3.33
3.04
3.09
3.11
3.18
However, we note that the MACS-only training results obtained the lowest performance on ESC50. We note that this is likely related to the differences in data distributions. To investigate this, we have computed Frechet Audio Distances (computed via CLAP embeddings) between ESC-50 and different subsets in Table 5 We observe that the highest distance is between MACs and ESC50 is the highest. This suggests that if the similarity between the target domain and the training set for the interpreter is relatively high, is possible to train LMAC-ZS on smaller subsets.
In Table we also present the performance of additional baselines, ScoreCAM and GScoreCAM [49], and we see that on ESC50, except the case where input audio is contaminated with another audio sample from the ESC50 dataset, LMAC-ZS is able outperform these baselines for the majority of the faithfulness metrics.
18

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NeurIPS Paper Checklist
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Answer: [NA]
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URL.
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assets are released, the license, copyright information, and terms use in the package should be provided. For popular datasets, paperssi thcode. com datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
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Answer: [Yes]
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25


Reviews:
[{"review_id": "cC2l2jtfyw", "reviewer": "Reviewer_Tsn7", "summary": "In this paper, the authors focus on interpreting the decisions of zero-shot audio classifiers, particularly ones based on the Contrastive Language-Audio Pretraining (CLAP) model. To achieve this, the authors propose to learn a decoder that predicts a \"listenable\" audio saliency map (a mask on the input spectrogram) from an audio-text pair. The decoder is trained with a novel loss function that remains faithful to the characteristics of CLAP features. The authors demonstrate the effectiveness of their approach with extensive experiments across various datasets.", "questions": "### Questions\n1. **line 147** *\"The intuition is that the similarity between two text prompts should be reflected in the similarity of the audio embeddings from the corresponding masked spectrograms\"*.\n\n    Equation (7) is the only part of the loss function that is distinctly novel, as it adds a third term to the original loss in [8]. Have you tried training the overall decoder without the third term? It would be helpful to the paper if you can show that adding the third term yields a noticeable improvement over just using the first two terms; otherwise, the hypothesis remains unsupported by evidence.\n\n2. It is common to perform zero-shot classification with foundation models trained on a large dataset. Suppose we have one such hypothetical model, trained on a dataset with millions of samples.\n    - Is it necessary to train the decoder on the same large dataset? If not, can an estimation be made as to what percentage of the training data the decoder needs to see to achieve reliable performance? (For instance, it may be the case that after seeing 20% of the data, the model achieves 80% of its full performance).\n    - If it is necessary to train the decoder on the pre-training dataset, using LMAC-ZS becomes expensive in many cases. Can LMAC-ZS be jointly learned during the pre-training process of the base model (e.g. CLAP)?\n        - Note that this may not be so straightforward, as the first term in the loss objective will try to match the decoder predictions to faulty entries of $C$ at the start of the training.\n\n### General Suggestions\n- **line-114**; **line-122**: The authors note that they omitted a part of Equation (4) for brevity. I strongly suggest against doing this; *clarity* is more important than brevity when posing a loss function or optimization objective. \n\n    Equation (4) is supposed to be a min-max objective; the goal is to maximize the classification confidence of the masked-in (salient) part of the audio, while minimizing the confidence of the masked-out part. Thus the whole equation should be written together, and parts of it should not be omitted. If brevity is desired, it may be achieved by abbreviating $\\text{CrossEntropy}$ to $CE$ or $\\mathcal{L}_{CE}$.\n\n- **line 120:** The abbreviation L-MAC is used, but it has not been defined previously. The authors should define the abbreviation on line 70.\n- **line 62-63:** The citation style is inconsistent. For example, in lines 59-60, it has been written \"Key approaches in this category include [19, 20, 21, 22],\". The same citation style can be followed in line 64: \"Notable attempts in this vein include [23, 24, 25].\"\n\n---\n[b] Shimada, Kazuki, et al. \"Zero-and Few-Shot Sound Event Localization and Detection.\" ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024.", "limitations": "### Limitations\n- One limitation that I feel is not addressed is that the approach needs to be specifically trained on CLAP's data; it is not plug-and-play like Grad-CAM or similar approaches.\n    - In **line 216** It is mentioned that the decoder uses CNN14 layers, presumably because the audio part of CLAP is based on CNN14.\n        - Now if we have another zero-shot audio classifier, LAION-CLAP, that (suppose) has the same dataset but a different transformer-like architecture, then the decoder may not be transferable. Another decoder architecture needs to be designed to suit the alternative zero-shot classifier. So for different zero-shot foundation models, it may become necessary to have different architectures, which can be a hurdle.\n        - As noted earlier, for same architecture but different datasets, it is still needed to train the decoder again. Unless of course, the decoder from one pre-training dataset transfers to another dataset, as a general purpose audio method.\n\nApart from these scalability concerns, I believe the remaining limitations are adequately addressed.\n\n---\n\nUpdate: The rebuttal addresses most of my concerns, so I will raise my score to 6.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "### Strengths\n\n1. Although there is a significant body of research focusing on interpretability of audio classifiers, interpretability of *zero-shot* audio classifiers (and zero-shot classifiers in general) is an under-explored area, which certainly merits more investigation.\n2. From a technical perspective, the proposed approach is sound and has no glaring weaknesses.\n3. The qualitative and quantitative results show that the method indeed works as claimed.\n    - Particularly the anonymized qualitative samples provided are highly appreciated.", "weaknesses": "### Weaknesses\n\n1. Even after reading two out of the three subsections in Section 2: Methodology, a reader learns nothing new from the paper.\n\n    To be more specific:\n    - Section 2.1 provides a high-level overview of \"Contrastive Language Audio Pretraining\" (CLAP) [10], while Section 2.2 gives a brief summary of the approach of the paper \"Listenable Maps for Audio Classification\" (L-MAC) [8]. Both these sections discuss the previous papers as is, without any new perspective.\n    - It is solely Section 2.3 that pertains to the method proposed by the paper. Even in Section 2.3, the sub-subsection @line-152, **\"Producing Listenable Interpretations\"** is not something new achieved by the proposed method, but rather a feature of the previous L-MAC paper [8].\n        - Notably, [8] also has an explicit sub-section with the *exact same title*; **Section 2.2: Producing Listenable Interpretations**. The authors make no effort in lines 152-156 to clarify that the \"listenable\" feature comes from [8]; thus this is not only redundant, but also very close to plagiarism.\n\n    - **Suggestion:** I strongly suggest that Sections 2.1, 2.2, and the sub-subsection (@lines 152-156) be discussed separately as a \"Background\" or \"Preliminaries\" section, instead of Methodology. Specifically, since the proposed approach builds significantly upon L-MAC [8], it is necessary to disentangle your contributions from that of the authors of [8].\n\n2. The technical novelty is limited by the L-MAC paper.\n    - From my understanding, the only novelty in the method is the loss function in Equation 6. And even that is mostly changing the cross entropy-based objectives in L-MAC (Equation (2) in [8]) to contrastive losses characteristic of CLAP. Overall, the same min-max objective is retained, and the same regularization term is added for mask sparsity. The same general framework from L-MAC is followed: a decoder is trained on the same dataset as the encoder to predict a mask on the input audio spectrogram.\n\n3. (minor weakness) Comparison against baselines.\n\n    - The authors are comparing against the baselines of GradCAM, GradCAM++, SmoothGrad, Integrated Gradients. While all these methods were also used for comparison with the original L-MAC in [8], that was in a regular classification setting; these methods are, by nature, poorly suited to zero-shot settings.\n\n    - For instance, for both \"cat\" and \"glass-breaking\", the CLAP model needs to \"look\" at the important regions of the audio to give an encoded audio feature---which can be observed in Figure 4 (for GradGAM) in the paper.\n\n    - **Suggestion**: A better baseline would perhaps be from the paper \"gScoreCAM: What objects is CLIP looking at?\" [a]. The paper empirically establishes that for CLIP, in zero-shot settings, ScoreCAM performs better than the other forms of CAM.\n\n    - *Note:* I understand that in general, there is a lack of baselines to compare to (for instance, listen-to-interpret and L-MAC are not applicable). I do not expect the authors to conduct any additional experiments.\n\n---\n\nIn general, the paper has a significant amount of similarity/redundancy with the L-MAC paper [8], from the naming of sections and content (noted in Weakness 1), figures (Fig 1 in [8] and fig 2 in current paper), and even the exact set of metrics posed in the identical order in Section 3.1, which makes it difficult to discern the novelty and contributions.\n\n---\n[a] Chen, Peijie, et al. \"gscorecam: What objects is clip looking at?.\" Proceedings of the Asian Conference on Computer Vision. 2022.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your comments. Our replies are below:  \n\n- Regarding your comments on the organization of the paper, and the fact that the paper contains sections pertaining to L-MAC and CLAP: \n\n    Our aim was to make this submission as self-contained as possible, and to provide the reader with the necessary preliminaries. We agree with your suggestion, and we will put Sections 2.1 and 2.2 under a new section called ‘Preliminaries’. We have also noted your comment on the subsection called ‘Producing Listenable Interpretations’, and we will clarify the overlaps with the original L-MAC paper. \n\n- Regarding your comment on limited novelty on top of L-MAC: \n\n  This submission is an extension of the L-MAC framework to zero-shot classifiers, and hence it is called L-MAC-ZS. There are obvious similarities, like L-MAC, L-MAC-ZS is also a masking method that uses a decoder. However, beyond using a decoder network, using masking, and the fact that the masking is done in the STFT domain (to be able to produce listenable explanations) this paper addresses another, and more challenging problem compared to the original L-MAC paper. \n   \n  We would like to also state that, to the best of our knowledge, this is the first method that explores using a decoder based method to explain an audio foundation model like CLAP. Also, note that the loss function is significantly different from L-MAC. In L-MAC, the loss function tries to maximally retain logit similarity for mask-in, minimize the logit similarity for mask-out.  In L-MAC-ZS however the interpreter is not trained with data with a fixed set of labels, and the loss function aims to match the original similarities between the text prompts and the audios in the batch, with the similarities obtained after masking. We also introduced a unimodal similarity term in Eq. 7, which we show is important for our results in this rebuttal (Tables 1 and 2 in the general rebuttal) . \n\n- Comparisons with baselines: \n\n   We thank you for your suggestion to compare with gScoreCAM. We have added a comparison with gScoreCAM and ScoreCAM in Tables 1 and 2 of the general rebuttal. We observe that both gScoreCAM and ScoreCAM faithfulness scores are lower than L-MAC-ZS. \n\n\n- Regarding your question whether using the third term (given in equation 7) in the loss function is useful: \n\n   We have conducted a qualitative (the rebuttal .pdf file) and quantitative ablation study (Tables 1 and 2 in the general rebuttal). We observe that by introducing the unimodal alignment loss term explanations become sensitive to the text prompt (Figure 3 in the pdf) while maintaining comparable faithfulness metrics (Tables 1 and 2). This is confirmed qualitatively as we note that explanations generated without the unimodal diversity term remain unchanged regardless of the provided text prompt.\n\n\n- Regarding your question on if we need to train the decoder on the same dataset as the foundation model to be interpreted (e.g. CLAP): \n\n   Please note that we had partially investigated this in the submission. In Tables 1, 2, 3 of the original manuscript we have provided results with a decoder that had only been trained with Clotho (approximately ¼ of the whole training dataset). We see that results do not significantly drop. To further investigate this, we conducted an ablation study on the amount of data needed to train the decoder (Tables 3 and 4 in the general rebuttal). We experimented with randomly sampled subsets of the CLAP dataset and with the individual datasets comprising the CLAP training data (namely AudioCaps, FSD50K, MACs, and Clotho). As observed in the submission, we note that the explanation’s faithfulness is comparable when training the decoder on the full training data or a subset. The MACs-only training results in the lowest performance on ESC50. We note that this is likely related to the differences in data distributions. In fact, the MACs / ESC-50 Frechet Audio Distance (computed using CLAP embeddings) is the highest among all the subsets (Table 6). We conclude that training on a subset of the training data, the decoder can generate faithful explanations for the ZS classifier.\n\n\n- > If it is necessary to train the decoder on the pre-training dataset, using LMAC-ZS becomes expensive in many cases. Can LMAC-ZS be jointly learned during the pre-training process of the base model (e.g. CLAP)?\n\n   In this paper we have not experimented with jointly training CLAP and L-MAC-ZS. However, this is an interesting future direction. Also, please note that in response to your earlier comment, it is also possible to train LMAC-ZS on a subset of the pre-training dataset. \n\n- Regarding your comment,\n> line-114; line-122: The authors note that they omitted a part of Equation (4) for brevity. I strongly suggest against doing this; clarity is more important than brevity when posing a loss function or optimization objective.\n   \n   We will include the full loss function in Equation 4. \n\n- Regarding your comments on citation style consistency, and undefined abbreviation in line 120: \n\n  We will fix these issues in the final version of the paper. \n\n- Regarding your comment:\n> One limitation that I feel is not addressed is that the approach needs to be specifically trained on CLAP's data; it is not plug-and-play like Grad-CAM or similar approaches. \nand your related comments regarding generalizability to different foundation model architectures, and different datasets:\n\n   Generalizability to different audio encoder architectures: There’s no theoretical reason as to why the convolutional decoder would not provide faithful and understandable explanations for different foundation model architectures. However, it also remains feasible to design new decoder architectures based for a different foundation model. Regarding different training datasets: As we mentioned earlier above, we have experimented with training the decoder with subsets of the training data, and we see that the results remain comparable.", "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": "PVmQS9URRc", "reviewer": "Reviewer_vLBn", "summary": "The paper introduces a post-hoc interpretation method for zero-shot audio classifiers, named LMAC-ZS (Listenable Maps for Audio Classifiers in the Zero-Shot context). It addresses the challenge of interpreting predictions from zero-shot audio classifiers that define audio classes based on textual prompts, where labels are not predefined but generated dynamically. LMAC-ZS outputs saliency maps that highlight important regions within input audio, correlating these with corresponding text prompts. The method involves a novel loss function that maintains the original audio-text similarity, enhancing interpretability. Experiments were conducted using the CLAP model on standard datasets like ESC50 and UrbanSound8K.", "questions": "1. In Figure 7, the visual explanations for 'Toilet flushing' and 'Water drops' look very similar despite different similarity scores (Sim=0.69 and Sim=0.14). How does the model ensure distinguishable and meaningful explanations, and can more distinct examples be provided to demonstrate the method's effectiveness?\n2. I have a question about a set of textual prompts. What happens if there are sounds that are not in the text?", "limitations": "The author has addressed the limitations.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Motivation is Clearly articulated. It stresses the importance of interpretability in AI, particularly for models used in critical decision-making areas, focusing on the less-explored zero-shot audio classification.\n2. The problem of providing interpretable explanations for zero-shot audio classifiers is clearly formulated and identified as a novel challenge in the field.\n3. The paper compares LMAC-ZS with several baseline methods like GradCAM++, SmoothGrad, and Integrated Gradients, providing a thorough comparative analysis and justifying the selection based on their relevance and common use in related tasks.", "weaknesses": "1. It is observed that in some scenarios LMAC-ZS (CT) performs better than LMAC-ZS (FULL) in metrics such as AI, AD, and AG. This is counterintuitive, as one would expect the model trained on the full CLAP dataset to perform better. An explanation for this discrepancy is needed.\n2. The paper mentions exploring the training of LMAC-ZS only on the Clotho dataset to simulate a limited computational budget scenario. It raises the question of whether training on other individual datasets or comparing the performance across different single datasets versus the full dataset would yield different insights. This aspect needs further exploration and clarification.\n3. In Table 2, the use of ESC50 contamination for ZS classification on the ESC50 dataset seems confusing. The rationale behind using the same dataset for contamination needs to be explained in detail. Similarly, in Table 3, the reason for using US8K contamination for Mel-Masking and ESC50 contamination for STFT-Masking in ZS classification on US8K requires clarification.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your constructive and positive comments. Our replies are below:\n\n- Regarding your comment as to the discrepancy between the L-MAC-ZS Full and L-MAC-ZS (CT) in some cases: \n\n  This is potentially due to optimization, and choice of hyperparameters (note also that this is mainly observed for mel-masking and not STFT masking). From preliminary results on training L-MAC-ZS (Full) for few more epochs for mel-masking, and we see that the results seem to be on par. \n \n- Regarding your comment on training L-MAC-ZS with other subsets (subsets other than Clotho):\n\n  In Tables 3 and 4 (the general reply above) we report the results obtained by training the decoder on different subsets. We experimented both with randomly sampled subsets of the whole CLAP dataset and with the individual datasets comprising the CLAP training data (namely AudioCaps, FSD50K, MACs, and Clotho).  As mentioned above, we note that the explanation’s faithfulness is comparable when training the decoder on the full training data or a subset. We note that the training with only MACs dataset results in the lowest performance on ESC50. We note that this is likely related to the differences in data distributions. In fact, the MACs / ESC-50 Frechet Audio Distance (computed using CLAP embeddings) is the highest among all the subsets (Table 6 in the general rebuttal above).  We conclude that training on a subset of the training data, the decoder can generate faithful explanations for the ZS classifier.\n\n- Regarding your comment: \n > In Table 2, the use of ESC50 contamination for ZS classification on the ESC50 dataset seems confusing. \n\n  This setting refers to the case where we create sound mixtures using two recordings with different classes from ESC50. Same is true for the US8k dataset as well. The goal is to investigate whether L-MAC-ZS is able to point out the important parts of the recording in a more complicated audio recording. \n\n- Regarding your comment: \n> In Figure 7, the visual explanations for 'Toilet flushing' and 'Water drops' look very similar despite different similarity scores (Sim=0.69 and Sim=0.14). How does the model ensure distinguishable and meaningful explanations, and can more distinct examples be provided to demonstrate the method's effectiveness? \n\n  We have provided additional qualitative examples in the .pdf document provided for the general rebuttal. \n\n- Regarding your question \n  > I have a question about a set of textual prompts. What happens if there are sounds that are not in the text?\n\n  In this case, as demonstrated in the example in Figure 4 and more generally Figure 3, L-MAC-ZS is able to return an empty mask when the similarity between the text prompt and the input audio is low. We have provided additional evidence to this in the pdf attached to the general 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": "MAm5bAYXE1", "reviewer": "Reviewer_DqWc", "summary": "This paper describes an extension of the LMAC method for explaining decisions made by audio classifiers.  The novelty in the present article is to extend from fixed-vocabulary settings to zero-shot / open text description settings.  To accomplish this, the authors propose a training objective that aims to preserve proximity of (learned) masked audio embeddings to embeddings of corresponding text descriptions.  The method is evaluated on the CLAP model over two standard sound event detection datasets, and compares quite favorably to prior work along several evaluation criteria.", "questions": "My questions primarily center around equation 6:\n\n- Notational quirk: $C_{i,j}$ is a scalar value (equation 5), right?  Why are norm bars ($\\|$) used in eq 6 when computing the loss on approximating $C_{i,j}$?\n- Reasoning through the first term of eq6, a few things jump out at me.  First, expanding out the definition in eq5, the summand should be equivalent to $\\left|t_i^\\mathsf{T} \\left(f_\\text{audio}(X_{\\text{audio},j}) - f_\\text{audio}(M_\\theta(t_i, h_j) \\odot X_{\\text{audio},j}) \\right)\\right|$.  That is, the gap between the original and masked audio embedding should be small (ie the mask doesn't do much) or approximately orthogonal to $t_i$, and this should hold for all $t_i$ in batch when the outer sum is computed.  \n    - What is the effect of the batch size / batch diversity on this loss?  Presumably, the more (and more diverse) text embeddings we compute against, the harder it will be to achieve this orthogonality while having nontrivial masks.  Similarly, a too-small batch could make it too easy to achieve this orthogonality in a high-dimensional embedding space while not producing useful masks.  Is there a sweet spot or range sizes where this seems to work (on CLAP)?\n- What happens if the third term is left out of the loss?  Similarly, how sensitive is this term to size and diversity of the batch?\n\nA couple of minor questions:\n\n- Line 155 describes the audio reconstruction by ISTFT.  It's not clear how phase is treated here, since $X$ appears to be magnitude spectra.  Presumably phase is retained from the STFT and propagated through, but if some other method is used here (eg griffin-lim or the like) it should be made explicit.\n- In general, the spectrogram visualizations (eg figure 4) are quite difficult to read in this paper.  It would be helpful to A) label the axes with appropriate units, B) use a log-scale frequency axis for display (even if the underlying method uses linear spectra, the results will be more visible), and C) use a log amplitude scaling on the magnitudes to enhance visual contrast.", "limitations": "The limitations and impacts are well and appropriately described by the authors.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Overall, I found this paper to be well written and easy to follow.  The topic of the paper is timely and relevant, as much audio analysis research does seem to be trending toward open vocabulary settings.  The empirical evaluations are appropriate and generally convincing.\n\nThe proposed method makes a lot of intuitive sense, and appears to be a natural extension of the fixed vocabulary setting (section 2.2).", "weaknesses": "The main weakness I see in this work is a somewhat shallow investigation of the proposed method itself, as opposed to high-level comparisons to prior work.  The core technical contribution is described in equations 5-7, and consists of 3 terms: one to approximately preserve the embedding of audio (relative to text embedding) after masking, one to promote sparsity on the masks, and one to promote diversity of masks when conditioning on different text embeddings.  As in CLAP, contrast is obtained by comparing amongst other samples within the training batch.  This raises a handful of questions about the various components of the training objective which I will detail below; however, the critical issue here is that no ablation study is conducted to measure the importance of the various terms.  This leads the reader at a bit of a loss to understand how these components interact with each other, whether they're all necessary, and so on.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your comments. Our replies are below: \n\n- Regarding your comment on the lack of ablation study regarding different terms of the loss function: \n\n  We have conducted an ablation study (both quantitative and qualitative) to showcase the relevance of the unimodal diversity loss introduced in Eq. 7.\n\n- Regarding the notational quirk on $C_{ij}$ you make reference to:\n  \n  You are correct. We will remove the norm bars in Equation 6. \n\n- Regarding your comment on the first term in equation 6, and whether a trivial, all-ones mask would minimize this loss function: \n\n  It definitely would. Because of this, the second term in Equation 6 is very important, and as we indicate in line 142 of the submission, this term is needed to avoid trivial solutions (all-ones mask) :  `The second term in Equation 6 promotes sparsity in the generated mask to avoid trivial solutions`. \n\n- Regarding your comment, \n  > What happens if the third term is left out of the loss?\n\n  The third term in the loss function is responsible for making the decoder more sensitive to text prompts. To validate its impact on the generated explanations, we have carried out a qualitative and quantitative ablation study for this. Please see the qualitative results in the .pdf document attached for the rebuttal. The quantitative comparison indicates that by introducing the third loss term we marginally decrease the explanation’s faithfulness, while increasing the sensitivity to text prompts significantly (Fig 3 in the attached pdf).\n\n- > Similarly, how sensitive is this term (third term) to size and diversity of the batch?\n\n  First of all, note that the proposed loss function is not contrastive. It is rather a loss term that matches two matrices. So, the behavior is expected to be more similar to regular loss functions. To quantify the effect of batch size on the end performance of the model, we have also trained a model with BS=4 on the AudioCaps dataset and reported the results in Table 5. We observe that with BS=4 in fact the faithfulness numbers are slightly worse compared to BS=2 (potentially due to suboptimal hyperparameters).\n\n\n- Regarding your comment, \n> Line 155 describes the audio reconstruction by ISTFT. It's not clear how phase is treated here, since appears to be magnitude spectra. Presumably phase is retained from the STFT and propagated through, but if some other method is used here (eg griffin-lim or the like) it should be made explicit.\n\n  The phase of the original input audio is used to reconstruct the listenable audio for the explanation (same way it is done in the original L-MAC paper, equation 3.) We will clarify this in the final version of the paper.  \n\n- Regarding your comments, \n> In general, the spectrogram visualizations (eg figure 4) are quite difficult to read in this paper. It would be helpful to A) label the axes with appropriate units, B) use a log-scale frequency axis for display (even if the underlying method uses linear spectra, the results will be more visible), and C) use a log amplitude scaling on the magnitudes to enhance visual contrast.\n\n  Thank you for your comment. We will use a log-amplitude scaling on the magnitudes / log-scale frequency axis for display, and label the axes.", "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:
We thank all the reviewers for their comments. We provide a reply to each reviewer in the corresponding rebuttal. Here, we report the additional quantitative results obtained during the rebuttal period to address the reviewer's concerns. The reviewer replies refer to the table numbers listed below. 

**Table 1.**  In-Domain Results on ESC-50, **LMAC-ZS (CT)** refers to LMAC-ZS trained on Clotho (with the diversity loss term), **LMAC-ZS (CT) NoDiv** refers to LMAC-ZS trained on Clotho without the diversity term (Eq. 7 in the paper). We only show the baselines that perform close to LMAC-ZS. 

| **Metric**                  | AI (↑) | AD (↓) | AG (↑) | FF (↑) | Fid-In (↑) | SPS (↑) | COMP (↓) | MM   |
|-----------------------------|--------|--------|--------|--------|------------|---------|----------|------|
| GradCam                     | 20.30  | 23.75  | 7.77   | 0.78   | 0.58       | 0.72    | 11.54    | 0.14 |
| GradCam++                   | 32.50  | 8.97   | 7.95   | 0.79   | 0.84       | 0.41    | 12.41    | 0.35 |
| ScoreCAM                    | 29.97  | 12.14  | 8.82   | 0.70   | 0.75       | 0.32    | 12.59    | 0.41 |
| GScoreCAM                   | 29.64  | 8.56   | 6.62   | 0.79   | 0.84       | 0.36    | 12.52    | 0.39 |
| **LMAC-ZS (CT)**            | 37.40  | 7.43   | 11.26 | 0.78   | 0.86       | 0.50    | 12.29 | 0.11 |
| **LMAC-ZS (CT) NoDiv**      | 37.54  | 6.38 | 11.70| 0.77   | 0.88   | 0.72    | 11.59 | 0.02 |

**Table 2.** Out-of-Domain Results on ESC50 Mixtures 

| **Metric**                  | AI (↑) | AD (↓) | AG (↑) | FF (↑) | Fid-In (↑) | SPS (↑) | COMP (↓) | MM   |
|-----------------------------|--------|--------|--------|--------|------------|---------|----------|------|
| GradCam                     | 23.77  | 25.25  | 12.24  | 0.69   | 0.49       | 0.69    | 11.73    | 0.17 |
| GradCam++                   | 29.52  | 14.84  | 10.17  | 0.70 | 0.70       | 0.39    | 12.48    | 0.35 |
| ScoreCAM                    | 31.39  | 7.03 | 7.05   | 0.79   | 0.87   | 0.36    | 12.52    | 0.39 |
| GScoreCAM                   | 28.07  | 13.74  | 8.42   | 0.70   | 0.73       | 0.32    | 12.59    | 0.41 |
| **LMAC-ZS (CT)**            | 35.65  | 12.23  | 13.04  | 0.69   | 0.74       | 0.53    | 12.18    | 0.09 |
| **LMAC-ZS (CT) NoDiv**      | 37.35 | 9.55   | 13.53 | 0.67   | 0.79       | 0.75 | 11.46 | 0.02 |


**Table 3.** Ablation of the amount of training data (the model is trained only on the indicated dataset) - In-Domain Setting (ESC50)

| **Metric**                  | AI (↑) | AD (↓) | AG (↑) | FF (↑) | Fid-In (↑) | SPS (↑) | COMP (↓) | MM   |
|-----------------------------|--------|--------|--------|--------|------------|---------|----------|------|
| LMAC-ZS Clotho          | 37.40  | 7.43   | 11.26  | 0.78   | 0.86       | 0.50    | 12.29    | 0.11 |
| LMAC-ZS FSD50K          | 34.00  | 8.33   | 10.12  | 0.77   | 0.83       | 0.61    | 11.83    | 0.04 |
| LMAC-ZS AudioCaps       | 39.00  | 5.93   | 10.43  | 0.78   | 0.88       | 0.68    | 11.67    | 0.07 |
| LMAC-ZS MACs            | 15.61  | 22.86  | 5.32   | 0.78   | 0.61       | 0.42    | 12.42    | 0.04 |
| LMAC-ZS Subset (25%)    | 41.50  | 3.48   | 7.99   | 0.79   | 0.92       | 0.65    | 11.91    | 0.22 |
| LMAC-ZS Subset (50%)    | 43.70  | 3.54   | 7.86   | 0.79   | 0.91       | 0.63    | 11.97    | 0.19 |
| LMAC-ZS Subset (75%)    | 40.60  | 4.74   | 7.73   | 0.79   | 0.89       | 0.66    | 11.84    | 0.17 |
| LMAC-ZS CLAP Data       | 43.35  | 4.29   | 10.57  | 0.78   | 0.90       | 0.65    | 11.86    | 0.10 |

**Table 4.** Ablation of the amount of training data (the model is trained only on the indicated dataset)  - Out-of-Domain Setting (ESC50 Mixtures)

| **Metric**                  | AI (↑) | AD (↓) | AG (↑) | FF (↑) | Fid-In (↑) | SPS (↑) | COMP (↓) | MM   |
|-----------------------------|--------|--------|--------|--------|------------|---------|----------|------|
| LMAC-ZS Clotho          | 35.65  | 12.23  | 13.04  | 0.69   | 0.74       | 0.53    | 12.18    | 0.09 |
| LMAC-ZS AudioCaps       | 35.97  | 10.35  | 11.42  | 0.68   | 0.76       | 0.71    | 11.63    | 0.07 |
| LMAC-ZS FSD50K          | 26.95  | 16.26  | 9.97   | 0.67   | 0.65       | 0.66    | 11.59    | 0.03 |
| LMAC-ZS MACS            | 11.38  | 31.54  | 4.42   | 0.68   | 0.38       | 0.44    | 12.41    | 0.05 |
| LMAC-ZS Subset (25%)    | 42.65  | 5.99   | 9.81   | 0.70   | 0.84       | 0.66    | 11.90    | 0.20 |
| LMAC-ZS Subset (50%)    | 39.47  | 7.52   | 9.05   | 0.71   | 0.81       | 0.66    | 11.88    | 0.16 |
| LMAC-ZS Subset (75%)    | 40.42  | 7.07   | 8.84   | 0.70   | 0.83       | 0.68    | 11.80    | 0.16 |
| LMAC-ZS CLAP Data       | 39.47  | 8.28   | 11.81  | 0.69   | 0.80       | 0.67    | 11.79    | 0.09 |

**Table 5.** Ablation on the batch size

| **Metric**                  | AI (↑) | AD (↓) | AG (↑) | FF (↑) | Fid-In (↑) | SPS (↑) | COMP (↓) | MM   |
|-----------------------------|--------|--------|--------|--------|------------|---------|----------|------|
| AudioCaps BS=4 OOD          | 27.15  | 18.00  | 10.10  | 0.66   | 0.63       | 0.70    | 11.75    | 0.03 |
| AudioCaps BS=2 OOD          | 35.97  | 10.35  | 11.42  | 0.68   | 0.76       | 0.71    | 11.63    | 0.07 |
| AudioCaps BS=4 ID           | 30.00  | 12.03  | 8.92   | 0.76   | 0.78       | 0.64    | 11.75    | 0.06 |
| AudioCaps BS=2 ID           | 39.00  | 5.93   | 10.43  | 0.78   | 0.88       | 0.68    | 11.67    | 0.07 |

**Table 6.** Frechet Audio Distance between ESC-50 and the datasets, computed using CLAP representations
| Comparison                 | FAD |
|----------------------------|-------|
| MACs               | 3.33  |
| FSD50K                  | 3.04  |
| Clotho               | 3.09  |
| AudioCaps            | 3.11  |
| CLAP Subset (25%)    | 3.18  |

Meta-review: All reviewers agreed that the paper has merit, with the ever increasing need of interpretability in audio tasks. There were a few concerns in the experiments, e.g., raised by reviewer vLBn and DqWc, but they were resolved during the rebuttal. The authors also provide an extensive comparison in the rebuttal to showcase the performance of their approach.

I'm sharing some of the concerns from reviewer Tsn7. The paper reads too similar to Paissan et al. (2024). I'm surprised how little contrast has been made between this paper and Paissan et al. (2024). Given how similar the approaches are, the paper should make it absolutely clear that the idea (and even the experimental design) is taken from Paissan et al. (2024) and is extended it to the zero-shot setting. This will help make the novelty clear. Given the rebuttal, I'm confident that the authors will make the best effort to improve the writing and giving more credits to Paissan et al. (2024).
Decision: Accept (poster)