• Corpus ID: 17589207

Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms

@article{Lee2017SamplelevelDC,
  title={Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms},
  author={Jongpil Lee and Jiyoung Park and Keunhyoung Luke Kim and Juhan Nam},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.01789}
}
Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains. This approach was applied to musical signals as well but has been not fully explored yet. To this end, we propose sample-level deep convolutional neural networks which learn representations from very small grains of waveforms (e.g. 2 or 3 samples) beyond typical frame-level input representations… 

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References

SHOWING 1-10 OF 24 REFERENCES

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

The experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms the previous state-of-the-art methods on the MagnaTagATune dataset and the Million Song Dataset.

Automatic Tagging Using Deep Convolutional Neural Networks

The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data.

WaveNet: A Generative Model for Raw Audio

WaveNet, a deep neural network for generating raw audio waveforms, is introduced; it is shown that it can be efficiently trained on data with tens of thousands of samples per second of audio, and can be employed as a discriminative model, returning promising results for phoneme recognition.

Audio Deepdream: Optimizing raw audio with convolutional networks

This work has followed in the footsteps of Van den Oord et al and trained a network to predict embeddings that were themselves the result of a collaborative filtering model, which creates a chain of differentiable functions from raw audio to high level features.

Learning the speech front-end with raw waveform CLDNNs

It is shown that raw waveform features match the performance of log-mel filterbank energies when used with a state-of-the-art CLDNN acoustic model trained on over 2,000 hours of speech.

Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals

This paper proposes to embed the so-called "persistence landscape," a rather new topological summary for data, into a convolutional neural network (CNN) for dealing with audio signals, and shows that the resulting persistent Convolutional Neural Network (PCNN) model can perform significantly better than state-of-the-art models in prediction accuracy.

Convolutional Neural Networks-based continuous speech recognition using raw speech signal

The studies show that the CNN-based approach achieves better performance than the conventional ANN- based approach with as many parameters and that the features learned from raw speech by the CNN -based approach could generalize across different databases.

ImageNet classification with deep convolutional neural networks

A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

This work takes convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and finds images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class, and produces fooling images, which are then used to raise questions about the generality of DNN computer vision.

Visualizing Higher-Layer Features of a Deep Network

This paper contrast and compare several techniques applied on Stacked Denoising Autoencoders and Deep Belief Networks, trained on several vision datasets, and shows that good qualitative interpretations of high level features represented by such models are possible at the unit level.