Download Melody Line Detection and Source Separation in classical Saxophone Recordings We propose a system which separates saxophone melodies from composite recordings of saxophone, piano, and/or orchestra. The system is intended to produce an accompaniment sans saxophone suitable for rehearsal and practice purposes. A Melody Line Detection (MLD) algorithm is proposed as the starting point for a source separation implementation which incorporates known information about typical saxophone melody lines, acoustic characteristics and range of the saxophone in order to prevent and correct detection errors. By extracting reliable information about the soloist melody line, the system separates piano or orchestra accompaniments from the solo part. The system was tested with commercial recordings and a performance of 79.7% of accurate detections was achieved. The accompaniment tracks obtained after source separation successfully remove most of the saxophone sound while preserving the original nature of the accompaniment track.
Download Exploring Phase Information in Sound Source Separation Applications Separation of instrument sounds from polyphonic music recordings is a desirable signal processing function with a wide variety of applications in music production, video games and information retrieval. In general, sound source separation algorithms attempt to exploit those characteristics of audio signals that differentiate one from the other. Many algorithms have studied spectral magnitude as a means for separation tasks. Here we propose the exploration of phase information of musical instrument signals as an alternative dimension in discriminating sound signals originating from different sources. Three cases are presented: (1) Phase contours of musical instruments notes as potential separation features. (2) Resolving overlapping harmonics using phase coupling properties of musical instruments. (3) Harmonic percussive decomposition using calculated radian ranges for each frequency bin.
Download Re-Thinking Sound Separation: Prior Information and Additivity Constraint in Separation Algorithms In this paper, we study the effect of prior information on the quality of informed source separation algorithms. We present results with our system for solo and accompaniment separation and contrast our findings with two other state-of-the art approaches. Results suggest current separation techniques limit performance when compared to extraction process of prior information. Furthermore, we present an alternative view of the separation process where the additivity constraint of the algorithm is removed in the attempt to maximize obtained quality. Plausible future directions in sound separation research are discussed.