Impact of audio codec and quality on genre classificaton and BPM recognition in Essentia

Music Information Retrieval (MIR) is a field of research that focusses on extracting information from music related data. This includes the genre of music and the beats per minute (BPM) of a song. Pipelines that extract this information from music are called feature extractors. Essentia is a library for such feature extraction. Often, the audio codec and quality is not considered in research setups within the field of MIR, while this could have an influence on the results. Therefore the main research question is "How do different audio codecs and audio quality impact genre classification and beats per minute (BPM) recognition in Essentia?". To answer this, the genre has been narrowed down to rock and the chosen audio codecs are FLAC, MP3 LAME and OGG Voribs. In collaboration with Muziekweb, a Dutch music library that collects all music that has been released in The Netherlands, it was possible to gather music files in lossless format. To degrade the audio quality, classify songs and recognize BPM, python pipelines for codec conversion, rock genre classification and BPM recognition were created an ran on this data. It has been concluded that changes in audio codec and quality have an influence on genre classification and BPM recognition in Essentia. It has not been concluded which codec and quality is best to use in the field of MIR. Further research is needed to answer this.https://gitlab.ewi.tudelft.nl/cse3k-21q2-music-faithfulness/project-sjoerd-hulleman GitLab repository containing all code used for this research.CSE3000 Research ProjectComputer Science and Engineerin