Slides: Deep learning architectures for music audio classification: a personal (re)view

I was invited to give a talk to the Deep Learning for Speech and Language Winter Seminar at the UPC in Barcelona. Since UPC is the university where I did my undergraduate studies, it was a great pleasure to give a talk there!

Download the slides!

The talk was centered in my recent work on music audio tagging, which is available on arXiv and is summarized in these previous posts: deep learning architectures for music audio classification, and deep end-to-end learning for music audio tagging at Pandora.

Thanks to @DocXavi for the picture!

Deep learning architectures for audio classification: a personal (re)view

One can divide deep learning models into two parts: front-end and back-end – see Figure 1. The front-end is the part of the model that interacts with the input signal in order to map it into a latent-space, and the back-end predicts the output given the representation obtained by the front-end.

Figure 1 – Deep learning pipeline.

In the following, we discuss the different front- and back-ends we identified in the audio classification literature. Continue reading

Deep end-to-end learning for music audio tagging at Pandora

TL;DR – Summary:

Machine listening is a research area where deep supervised learning is delivering promising advances. However, the lack of data tends to limit the outcomes of deep learning research – specially, when dealing with end-to-end learning stacks processing raw data such as waveforms. In this study we train models with musical labels annotated for one million tracks, which provides novel insights to the audio tagging task since the largest commonly used (academic) dataset is composed of ≈ 200k songs. This large amount of data allows us to unrestrictedly explore different deep learning paradigms for the task of auto-tagging: from assumption-free models – using waveforms as input with very small convolutional filters; to models that rely on domain knowledge – log-mel spectrograms processed with a convolutional neural network designed to learn temporal and timbral features. Results suggest that, while spectrogram-based models surpass their waveform-based counterparts, the difference in performance shrinks as more data are employed.

We also compare our deep learning models with a traditional method based on feature-design, namely: the Gradient Boosted Trees (GBT) + features model. Results show that the proposed deep models are capable of outperforming the traditional method when trained with 1M tracks, however the proposed models under-perform the baseline when trained with only 100K tracks. This result aligns with the notion that deep learning models require large datasets for outperforming strong (traditional) methods based on feature-design.

Let’s see what our best performing model (a musically motivated convolutional neural network processing spectrograms) yields when fed with a J.S. Bach aria:

Top10: Human-labels
Female vocals, triple meter, acoustic, classical music, baroque period, lead vocals, string ensemble, major, compositional dominance of: lead vocals and melody.
Top10: Deep learning
Acoustic, string ensemble, classical music, baroque period, major, compositional dominance of: the arrangement, form, performance, rhythm and lead vocals.
Continue reading

ISMIR 2017 highlights

This has been my first ISMIR ever, and I am thrilled for being part of this amazing community. It was fun to put faces (and hight, and weight) to these names I respect so much!

All awarded papers were amazing, and these are definitely in my list of highlights:
  • Choi et al. – every time I re-read this paper I am more impressed about the efforts they put in assessing the generalization capabilities of deep learning models. This work defines a high evaluation standard for those working in deep auto-tagging models!
  • Bittner et al. proposes a fully-convolutional model for tracking f0 contours in polyphonic music. The article has a brilliant introduction drawing parallelisms between their proposed fully-convolutional architecture and previous traditional models – making clear that it is worth building bridges between deep learning works and previous signal processing literature.
  • Oramas et al. – deep learning enables to easily combine information from many sources, such as: audio, text or images. They do so by combining representations extracted from audio-spectrograms, word-embeddings and ImageNet-based features. Moreover, they released a new dataset: MuMu, with 147,295 songs belonging to 31,471 albums.
  • Jansson et al.‘s work proposes a U-net model for singing voice separation. It seems that adding connections between layers at the same hierarchical level in the encoder and decoder for reconstructing masked audio signals is a good idea since several papers already reported good results using this setup.

But there were many other inspiring papers.. Continue reading