Voice separation in Polyphonic Music: a Data-Driven Approach

نویسنده

  • Anna Jordanous
چکیده

Much polyphonic music is constructed from several melodic lines known as voices woven together. Identifying these constituent voices is useful for musicological analysis and music information retrieval; however, this voiceidentification process is time-consuming for humans to carry out. Computational solutions have been proposed which automate voice segregation, but these rely heavily on human musical knowledge being encoded into the system. In this paper, a system is presented which is able to learn how to separate such polyphonic music into its individual parts. This system uses a training corpus of several similar pieces of music, in symbolic format (MIDI). It examines the note pitches in the training examples to make observations about the voice structures. Quantitative evaluation was carried out using 3-fold validation, a standard data mining evaluation method. This system offers a solution to this complex problem, with a 12% improvement in performance compared to a baseline algorithm. It achieves an equal standard of performance to heuristic-based systems using simple statistical observations: demonstrating the power of applying data-driven techniques to the voice separation problem.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Voice Separation - A Local Optimisation Approach Voice Separation — A Local Optimisation Approach

Voice separation, along with tempo detection and quantisation, is one of the basic problems of computer-based transcription of music. An adequate separation of notes into different voices is crucial for obtaining readable and usable scores from performances of polyphonic music recorded on keyboard (or other polyphonic) instruments; for improving quantisation results within a transcription syste...

متن کامل

A Machine Learning Approach to Voice Separation in Lute Tablature

In this paper, we propose a machine learning model for voice separation in lute tablature. Lute tablature is a practical notation that reveals only very limited information about polyphonic structure. This has complicated research into the large surviving corpus of lute music, notated exclusively in tablature. A solution may be found in automatic transcription, of which voice separation is a ne...

متن کامل

Voice Separation - A Local Optimization Approach

Voice separation, along with tempo detection and quantisation, is one of the basic problems of computer-based transcription of music. An adequate separation of notes into different voices is crucial for obtaining readable and usable scores from performances of polyphonic music recorded on keyboard (or other polyphonic) instruments; for improving quantisation results within a transcription syste...

متن کامل

Separating Voices in Polyphonic Music: A Contig Mapping Approach

Voice separation is a critical component of music information retrieval, music analysis and automated transcription systems. We present a contig mapping approach to voice separation based on perceptual principles. The algorithm runs in O(n) time, uses only pitch height and event boundaries, and requires no user-defined parameters. The method segments a piece into contigs according to voice coun...

متن کامل

Automatic Transcription of Polyphonic Vocal Music

This paper presents a method for automatic music transcription applied to audio recordings of a cappella performances with multiple singers. We propose a system for multi-pitch detection and voice assignment that integrates an acoustic and a music language model. The acoustic model performs spectrogram decomposition, extending probabilistic latent component analysis (PLCA) using a six-dimension...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008