On the disjointess of sources in music using different time-frequency representations

This paper studies the disjointness of the time-frequency representations of simultaneously playing musical instruments. As a measure of disjointness, we use the approximate W-disjoint orthogonality as proposed by Yilmaz and Rickard [1], which (loosely speaking) measures the degree of overlap of different sources in the time-frequency domain. The motivation for this study is to find a maximally disjoint representation in order to facilitate the separation and recognition of musical instruments in mixture signals. The transforms investigated in this paper include the short-time Fourier transform (STFT), constant-Q transform, modified discrete cosine transform (MDCT), and pitch-synchronous lapped orthogonal transforms. Simulation results are reported for a database of polyphonic music where the multitrack data (instrument signals before mixing) were available. Absolute performance varies depending on the instrument source in question, but on the average MDCT with 93 ms frame size performed best. © 2011 IEEE

An algorithm for multi tempo music lyric transcription

Applied Thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018.This paper documents an attempt to create an algorithm for multi-tempo music lyric transcription. This paper reviews music information retrieval as a field of study and identifies music lyric transcription as a subset of the music information retrieval field. The difficulties of music lyric transcription are highlighted and a gap in knowledge in the field is identified. There are no algorithms for music transcription that are applicable to all forms of music; they are usually specialised by instrument or by genre. The author attempts to fill this gap by creating a method for multi-tempo music lyric transcription. The methodology used to achieve this goal is a three-step process of taking audio as input, processing it using the REPET separation technique, and transcribing the separated audio file. The result of this paper was a relative success, with the music being separated successfully and the lyrics being transcribed but with accuracy lost