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
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.
These last months have been very intense for us – and, as a result, three papers were recently uploaded to arXiv. Two of those have been accepted for presentation in ISMIR, and are the result of a collaboration with Rong – who is an amazing PhD student (also advised by Xavier) working on Jingju music:
This journal article summarizes the most relevant results we found throughout my master thesis research – namely, the results related to popular western music. However, in this thesis we also describe the first attempt of remixing orchestral music for improving CI users classical music experience. Although the results for orchestral music are not conclusive, they provide nice intuition for designing future experiments and might be valuable for researchers who are interested in that topic.
Abstract – Many researchers use convolutional neural networks with small rectangular filters for music (spectrograms) classification. First, we discuss why there is no reason to use this filters setup by default and second, we point that more efficient architectures could be implemented if the characteristics of the music features are considered during the design process. Specifically, we propose a novel design strategy that might promote more expressive and intuitive deep learning architectures by efficiently exploiting the representational capacity of the first layer – using different filter shapes adapted to fit musical concepts within the first layer. The proposed architectures are assessed by measuring their accuracy in predicting the classes of the Ballroom dataset. We also make available the used code (together with the audio-data) so that this research is fully reproducible.