Deep Learning Methods for Musical Instrument Separation and Recognition. Carlos Lordelo.Oct-21 MIPF скачать в хорошем качестве

Deep Learning Methods for Musical Instrument Separation and Recognition. Carlos Lordelo.Oct-21 MIPF

Deep Learning Methods for Musical Instrument Separation and Recognition. Carlos Lordelo – Queen Mary University of London & DoReMIR. MIP-Frontiers Final workshop, October 2021. Abstract. Automatically transcribing polyphonic music to a score is a challenging task and one of the most discussed topics in the Music Information Retrieval (MIR) community. In particular, when analysing recordings with multiple instruments, the transcription process becomes even more complex, because not only each note should have its pitch and duration properly estimated, but the information regarding the timbre of sounds should also be correctly processed. It is mandatory to have a way of recognising the instrument that played each note and associate each sound to the correct voice in the final staff notation. Moreover, when dealing with signals with pitched and non-pitched sounds (drum kits), drum detection and classification is often performed independently because the sound characteristics of drum instruments differ in many aspects from pitched instruments that constitute the melodic and harmonic nature of music. With the final objective of allowing a more general multi-instrument automatic music transcription, where not only harmonic, but also percussive instruments could be present in the signal, in this project we propose and deeply investigate deep-learning-based harmonic-percussive source separation and instrument recognition methods. We show that by musically motivated architectures can improve the performance of each of those tasks and with the usage of the Tap & Fiddle Dataset, a dataset curated as part of this project containing 28 stereo recordings of traditional Scandinavian fiddle tunes with accompanying foot-tapping, we show that unsupervised domain adaptation methods can help in cases where no labelled data is available for specific instruments. Regarding instrument recognition, we investigate the pitch streaming task and propose novel deep-learning-based methods that can perform the task when any multi-pitch estimations are provided as input to the system. More information on MIP-Frontiers: https://mip-frontiers.eu/