CMKD; CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification

CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification (arxiv 2022)

https://arxiv.org/pdf/2203.06760.pdf

Contents

1. Abstract
2. Introduction
3. Cross-Model Knowledge Distillation (CMKD)
   1. CNNs
   2. ASTs
   3. Difference btw CNN and AST
   4. Knowledge Distillation
4. Experiment Settings
   1. Datasets
   2. Training Settings
5. FSD50K experiments
6. Audioset and ESC-50 experiments

Abstract

Audio classification

• CNNs : have been the de-facto standard building block for end-to-end audio classification models
• Self-attention mechanisms : have been shown to outperform CNNs
  • ex) Audio Spectrogram Transformer (AST)

Cross-Model Knowledge Distillation (CMKD)

This paper, we find an intriguing interaction between the two very different models

\(\rightarrow\) CNN and AST models are good teachers for each other … via knowledge distillation (KD)

Experiments with this CNN/Transformer Cross-Model Knowledge Distillation (CMKD)

• achieve new SOTA performance on FSD50K, AudioSet, and ESC-50.

1. Introduction

(1) Audio classification

History

• (1) hand-crafted features & hidden Markov models (HMMs)

• (2) CNNs : aim to learn a direct mapping from audio waveforms or spectrograms to corresponding labels

• (3) Self-attention : outperform CNN

CNN vs. Transformer

• CNN :
  • built-in inductive biases
    • ex) spatial locality and translation equivariance
  • well suited to spectrogram based end-to-end audio classification.
• Transformer :
  • do not have such built-in inductive biases
  • learn in a more data-driven manner, making them more flexible.
  • perform better but less computationally efficient than CNN models on long audio input due to their \(O(n^2)\) complexity.

Intriguing interaction between the two very different models

\(\rightarrow\) CNN and AST models are good teachers for each other.

• via knowledge distillation (KD)
• performance of the student model improves & is better than the teacher model

Cross-Model Knowledge Distillation (CMKD)

• knowledge distillation framework between a CNN and a Transformer model

• (1) Distillation works bi-directionally

  • (a) CNN→Transformer & (b) Transformer→ CNN

  • ( in general ) in KD, the teacher needs to be stronger than the student

    \(\rightarrow\) but for CMKD, a weak teacher can still improve a student’s performance

• (2) Student outperforms the teacher after knowledge distillation

  • even when the teacher is originally stronger!

• (3) KD between two models of the same class leads to a much smaller or no performance improvement

• (4) Simple EfficientNet KD-CNN model with mean pooling outperforms the much larger AST model

  • on FSD50K and ESC50 dataset.

Contribution

1. first to explore bidirectional knowledge distillation between CNN and Transformer models
2. conduct extensive experiments on standard audio classification datasets & find the optimal knowledge distillation setting
3. Small and efficient CNN models match or outperform previous SOTA

2. Cross-Model Knowledge Distillation

( Architecture of CNN and AST models )

2-1. CNNs

2-2. AST

2-3. Main difference between these two classes of models

2-4. Knowledge distillation setting and notation

(1) CNNs

CNN model without attention module [20]

• best CNN model on the audio classification task

Procedures

• [ Input ] input audio waveform of \(t\) seconds is converted into a sequence of 128 dim log Mel filterbank (fbank) features computed with a \(25 \mathrm{~ms}\) Hanning window every \(10 \mathrm{~ms}\).
  • result : \(128 \times 100 t\) spectrogram ( = input to CNN )
• [ Output ] output of the penultimate layer = size \((\lceil 128 / c\rceil,\lceil 100 t / c\rceil, d)\) in frequency, time, and embedding dimension,
  • mainly use EfficientNet-B2 [21]
  • where \(c\) is the feature downsampling factor of the CNN
• [ Mean pooling ] time and frequency mean pooling
  • produce \(d\) dim spectrogram-level representation
• [ Final result ] via linear layer
  • sigmoid (for multi-label classification)
  • softmax (for single-label classification)

(2) Audio Spectrogram Transformers

Original AST model proposed in [11]

• has the best performance on the audio classification task.

Procedures

• [ Input ] same as CCN

  • converted to a \(128 \times 100 t\) spectrogram in the same way as the CNN model.

• [ Patching ] split the spectrogram

  • into a sequence of \(N\) patches of size \(16 \times 16\)
    • with an overlap of 6 in both time and frequency dimension
  • number of patches : \(N=12\lceil(100 t-16) / 10\rceil\)

• [ Flatten ] flatten each \(16 \times 16\) patch to a \(1 \mathrm{D}\) patch embedding of size \(d\)

  • via a linear projection layer ( = patch embedding layer )

• [ POS ] add a trainable positional embedding (also of size \(d\) ) to each patch embedding

• [ CLS token ] append a [CLS ] token at the beginning of the sequence

  The resulting sequence is then input to a standard Transformer encoder

• [ Output of the [CLS] token ]
  • serves as the audio spectrogram representation
• [ Final result ] via a linear layer
  • sigmoid (for multilabel classification)
  • softmax (for single-label classification)

Pretrained weight

For both CNN and AST models , use ImageNet pretraining

• from public model checkpoints

CNN weight: Channel difference

• image : 3-channel \(\leftrightarrow\) audio : 1-d spectrogram

• solution : average the weights corresponding to each of the 3 input channels of the vision model checkpoints

  ( = equivalent to expanding a 1-channel spectrogram to 3-channels with the same content, but is computationally more efficient )

(3) Difference between CNN and AST

see Introduction

(4) Knowledge distillation

Original knowledge distillation setup [28] with consistent teaching [29]

• this simple setup works better than the more complex attention distillation strategy [17] for the audio classification task.

Procedure

• step 1) Train the teacher model
• step 2) During the student model training, feed the input audio spectrogram with the exact same augmentations to teacher and student models (consistent teaching)

Loss for the student model training:

• \(\operatorname{Loss}=\lambda \operatorname{Loss}_g\left(\psi\left(Z_s\right), y\right)+(1-\lambda) \operatorname{Loss}_d\left(\psi\left(Z_s\right), \psi\left(Z_t / \tau\right)\right)\).
• Loss \(_g\) = ground truth loss
• Loss \(_d\) = distillation loss

Details

• teacher model is frozen during the student model training.

• for Loss \(_d\), use the Kullback-Leibler divergence as \(L_{o s s_d}\).
• only apply \(\tau\) on the teacher logits
• fix \(\lambda=0.5\) and do not scale it with \(\tau\).
• Loss functions :
  • CE loss & softmax : for single-label classification tasks such as ESC-50
  • BCE loss & sigmoid : for multi-label classification tasks such as FSD50K and AudioSet.

3. Experiment Settings

(1) Datasets

3 widely-used audio classification datasets

• FSD50K [32], AudioSet [33], and ESC-50 [34]

FSD50K dataset [32]

• collection of sound event audio clips with 200 classes
• ( train, val, eval ) = ( 373134, 4170, 10231 )
• variable length from 0.3 to 30 s with an average of 7.6s.
• sample audio at \(16 \mathrm{kHz}\) and trim all clips to 10s.

We use the FSD50K dataset for the majority of our experiments in this paper (Section 4) for three reasons.

• (1) allows more rigorous experiments, since it has an official training, validation, and evaluation split
• (2) publicly available dataset, thus easy to reproduce
• (3) moderate size (50K samples)
  • AudioSet (2M samples) and ESC-50 (2K samples)
  • allows us to conduct extensive experiments with our computational resources

AudioSet [33]

• collection of over 2 million 10-second audio clips excised from YouTube videos
• labeled with the sounds that the clip contains from a set of 527 labels.
• ( balanced training, full training, and evaluation ) = ( 22k, 2M, 20k )
• use AudioSet to study the generalization ability of the proposed method on larger datasets.

ESC-50 [34]

• consists of 2,000 5-second environmental audio recordings organized into 50 classes.
• use ESC-50 to study the transferability of models trained with the proposed knowledge distillation method

(2) Training Settings

4. FSD50K experiments

(1) Which model is a good teacher?

• Red : CNN
• Blue : AST

Findings 1) CNNs and ASTs are good teachers for each other

• While KD improves the student model performance in almost all settings, we find that models always prefer a different teacher

Findings 2) For both directions, the student model matches or outperforms its teacher

• ∗ denotes that student model outperforms teacher model

Findings 3) The strongest teacher is not the best teacher

• both CNN and AST perform better with a smaller (and weaker) teacher

Finding 4) Self-KD leads to smaller or no improvement

Finding 5) Iterative knowledge distillation does not further improve model performance

5. AudioSet and ESC-50 Experiments

(1) AudioSet Experiments

To study the impact of training data size…

\(\rightarrow\) conduct experiments on both the (1) balanced and (2) full training set

Goal : study the generalization of the proposed method

\(\rightarrow\) thus do not search the KD hyperparameters with AudioSet

( just re-use the optimal KD setting found with the FSD50K dataset )

Findings 1) CMKD works out-of-the-box

• KD works well on AudioSet for both CNN→AST and AST→CNN, demonstrating the proposed cross-model knowledge generalizes for audio classification tasks

• training KD models with more epochs can lead to further performance improvement

  ( consistent with our finding that KD models are less prone to overfitting )

Findings 2) KD leads to larger improvement on smaller dataset

• KD is more effective when the model is trained with the smaller balanced training set

  ( \(\because\) AST and CNN models get closer with more training data and cross-model knowledge distillation thus plays a smaller role )

Findings 3) The advantage of KD narrows after weight averaging and ensemble

Comparison with SOTA

(2) ESC-50 Experiments