Deep Neural Network Architectures for Polyphonic Pitch Detection in violin recordings

Abstract

Multiple pitch detection an important issue in music information retrieval (MIR) is used in many applications including note separation chord recognition and automatic music transcription all of which rely on a robust pitch estimation algorithm In recent years the use of neural networks for polyphonic pitch detection has been studied but there is room for improvement with respect to the pitch estimation of bowed string instruments In this thesis we investigate polyphonic pitch detection in violin recordings and apply three deep neural network (DNN) architectures for handling the problem of harmonic interference Specifically we adopt the RWC music database for training pitch estimation models to build training datasets including single notes and two-note three-note and four-note chords In addition we consider the playing techniques pizzicato and vibrato at five different intensity levels Based on the pitch range of violins we analyse the suitable parameters and architectures of the DNNs using customized octave bands In addition to the numbers of layers and nodes the most important differences of the three architectures are their input layers In Architecture Ⅰ (Arch-Ⅰ) each input octave band is considered independently Architecture Ⅱ (Arch-Ⅱ) which is an extension of Arch-Ⅰ combines the second harmonics of the current octave band In Architecture Ⅲ (Arch-Ⅲ) the input layer is composed of the current octave band and the estimation results of the lower octaves We develop another DNN of the pitch classes and apply it to Arch-Ⅰ and Arch-Ⅱ to extract the correct pitch We also use post-processing for pitch smoothing in the three architectures Our evaluations show that Arch-Ⅲ has outstanding performance in violin solos and duos and Arch-Ⅰ and Arch-Ⅱ have similar results in violin solos but Arch-Ⅱ performs better than Arch-Ⅰ in violin duos

Date of Award	2017 Aug 29
Original language	English
Supervisor	Wen-Yu Su (Supervisor)