Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features. To overcome this, the combination of multiple different feature sets bearing diverse characteristics is needed. We propose a new approach to extend the scope of the features: We transcribe audio data into a symbolic form using a transcription system, extract symbolic descriptors from that representation and combine them with audio features. With this method, we are able to surpass the glass ceiling and to further improve music genre classification, as shown in the experiments through three reference music databases and comparison to previously published performance results.
['Thomas Lidy', 'Andreas Rauber', 'Antonio Pertusa', 'José Manuel Iñesta Quereda']
Improving Genre Classification by Combination of Audio and Symbolic Descriptors Using a Transcription Systems.