An evaluation of methodologies for melodic similarity in audio recordings of Indian art music

Comunicació presentada a l'ICASSP 2015, International Conference on Acoustics, Speech, and Signal Processing, que es va celebrar els dies 19 al 24 d'abril de 2015 a Brisbane, Austràlia.We perform a comparative evaluation of methodologies for computing similarity between short-time melodic fragments of audio recordings of Indian art music. We experiment with 560 different combinations of procedures and parameter values. These include the choices made for the sampling rate of the melody representation, pitch quantization levels, normalization techniques and distance measures. The dataset used for evaluation consists of 157 and 340 annotated melodic fragments of Carnatic and Hindustani music recordings, respectively. Our results indicate that melodic fragment similarity is particularly sensitive to distance measures and normalization techniques. Sampling rates do not have a significant impact for Hindustani music, but can significantly degrade the performance for Carnatic music. Overall, the performed evaluation provides a better understanding of the processing steps and parameter settings for melodic similarity in Indian art music. Importantly, it paves the way for developing unsupervised melodic pattern discovery approaches, whose evaluation is a challenging and, many times, ill-defined task.This work is partly supported by the European Research Council under the European Union's Seventh Framework Program, as part of the Comp-Music project (ERC grant agreement 267583). JS acknowledges 2009-SGR-1434 from Generalitat de Catalunya and ICT-2011-8-318770 from the European Commission

An evaluation of methodologies for melodic similarity in audio recordings of Indian art music

Comunicació presentada a l'ICASSP 2015, International Conference on Acoustics, Speech, and Signal Processing, que es va celebrar els dies 19 al 24 d'abril de 2015 a Brisbane, Austràlia.We perform a comparative evaluation of methodologies for computing similarity between short-time melodic fragments of audio recordings of Indian art music. We experiment with 560 different combinations of procedures and parameter values. These include the choices made for the sampling rate of the melody representation, pitch quantization levels, normalization techniques and distance measures. The dataset used for evaluation consists of 157 and 340 annotated melodic fragments of Carnatic and Hindustani music recordings, respectively. Our results indicate that melodic fragment similarity is particularly sensitive to distance measures and normalization techniques. Sampling rates do not have a significant impact for Hindustani music, but can significantly degrade the performance for Carnatic music. Overall, the performed evaluation provides a better understanding of the processing steps and parameter settings for melodic similarity in Indian art music. Importantly, it paves the way for developing unsupervised melodic pattern discovery approaches, whose evaluation is a challenging and, many times, ill-defined task.This work is partly supported by the European Research Council under the European Union's Seventh Framework Program, as part of the Comp-Music project (ERC grant agreement 267583). JS acknowledges 2009-SGR-1434 from Generalitat de Catalunya and ICT-2011-8-318770 from the European Commission

Music similarity analysis using the big data framework spark

A parameterizable recommender system based on the Big Data processing framework Spark is introduced, which takes multiple tonal properties of music into account and is capable of recommending music based on a user's personal preferences. The implemented system is fully scalable; more songs can be added to the dataset, the cluster size can be increased, and the possibility to add different kinds of audio features and more state-of-the-art similarity measurements is given. This thesis also deals with the extraction of the required audio features in parallel on a computer cluster. The extracted features are then processed by the Spark based recommender system, and song recommendations for a dataset consisting of approximately 114000 songs are retrieved in less than 12 seconds on a 16 node Spark cluster, combining eight different audio feature types and similarity measurements.Ein parametrisierbares Empfehlungssystem, basierend auf dem Big Data Framework Spark, wird präsentiert. Dieses berücksichtigt verschiedene klangliche Eigenschaften der Musik und erstellt Musikempfehlungen basierend auf den persönlichen Vorlieben eines Nutzers. Das implementierte Empfehlungssystem ist voll skalierbar. Mehr Lieder können dem Datensatz hinzugefügt werden, mehr Rechner können in das Computercluster eingebunden werden und die Möglichkeit andere Audiofeatures und aktuellere Ähnlichkeitsmaße hizuzufügen und zu verwenden, ist ebenfalls gegeben. Des Weiteren behandelt die Arbeit die parallele Berechnung der benötigten Audiofeatures auf einem Computercluster. Die Features werden von dem auf Spark basierenden Empfehlungssystem verarbeitet und Empfehlungen für einen Datensatz bestehend aus ca. 114000 Liedern können unter Berücksichtigung von acht verschiedenen Arten von Audiofeatures und Abstandsmaßen innerhalb von zwölf Sekunden auf einem Computercluster mit 16 Knoten berechnet werden