How do deep convolutional neural networks learn from raw audio waveforms?
Prior work on speech and audio processing has demonstrated the ability to obtain excellent performance when learning directly from raw audio waveforms using convolutional neural networks (CNNs). However, the exact inner workings of a CNN remain unclear, which hinders further developments and improvements into this direction. In this paper, we theoretically analyze and explain how deep CNNs learn from raw audio waveforms and identify potential limitations of existing network structures. Based on this analysis, we further propose a new network architecture (called SimpleNet), which offers a very simple but concise structure and high model interpretability.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Cross-Representation Transferability of Adversarial Attacks: From Spectrograms to Audio Waveforms
This paper shows the susceptibility of spectrogram-based audio classifiers to adversarial attacks and the transferability of such attacks to audio waveforms. Some commonly used adversarial attacks to images have been app…
Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms
Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. However, the emergence of deep neural networks as efficient f…
Audio ClassificationClassificationEvent DetectionGeneral Classification+1Audiomer: A Convolutional Transformer For Keyword Spotting
Transformers have seen an unprecedented rise in Natural Language Processing and Computer Vision tasks. However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveform…
Keyword SpottingConvolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
Environmental audio tagging is a newly proposed task to predict the presence or absence of a specific audio event in a chunk. Deep neural network (DNN) based methods have been successfully adopted for predicting the audi…
Audio TaggingnnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks
Converting time domain waveforms to frequency domain spectrograms is typically considered to be a prepossessing step done before model training. This approach, however, has several drawbacks. First, it takes a lot of har…
CPUGPU