Download Chroma and MFCC Based Pattern Recognition in Audio Files Utilizing Hidden Markov Models And Dynamic Programming In this paper we present an algorithm to reveal the immanent musical structure within pieces of popular music. Our proposed model uses an estimate of the harmonic progression which is obtained by calculating beat-synchronous chroma vectors and letting a Hidden Markov Model (HMM) decide the most probable sequence of chords. In addition, MFCC vectors are computed to retrieve basic timbral information that can not be described by harmony. Subsequently, a dynamic programming algorithm is used to detect repetitive patterns in these feature sequences. Based on these patterns a second dynamic programming stage tries to find and link corresponding patterns to larger segments that reflect the musical structure.
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.