Feature Learning for Chord Recognition: The Deep Chroma Extractor.

Description

We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural net- works. It is trained to compute chroma features that en- code harmonic information important for chord recogni- tion, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and re- solve harmonic ambiguities. We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.

Period	08 Aug 2016
Event title	17th International Society for Music Information Retrieval Conference (ISMIR 2016),
Event type	Other
Location	New York, United States, New YorkShow on map