Improving Generalization for Polyphonic Piano Transcription
نویسندگان
چکیده
In this paper, we present methods to improve the generalization capabilities of a classification-based approach to polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances, and the independent classifications are temporally constrained via hidden Markov model post-processing. Semi-supervised learning and multiconditioning are investigated, and transcription results are reported for a compiled set of piano recordings. A reduction in frame-level transcription error score of 10% was achieved by combining multiconditioning and semi-supervised classification.
منابع مشابه
Context-Independent Polyphonic Piano Onset Transcription with an Infinite Training Dataset
Many of the recent approaches to polyphonic piano note onset transcription require training a machine learning model on a large piano database. However, such approaches are limited by dataset availability; additional training data is difficult to produce, and proposed systems often perform poorly on novel recording conditions. We propose a method to quickly synthesize arbitrary quantities of tr...
متن کاملA Comparison of Feed Forward Neural Network Architectures for Piano Music Transcription
This paper presents our experiences with the use of feed forward neural networks for piano chord recognition and polyphonic piano music transcription. Our final goal is to build a transcription system that would transcribe polyphonic piano music over the entire piano range. The central part of our system uses neural networks acting as pattern recognisers and extracting notes from the source aud...
متن کاملAdaptive oscillator networks for partial tracking and piano music transcription
This paper presents our recent work in developing a system for transcription of polyphonic piano music. Our goal is to build a system that would automatically transcribe polyphonic piano music from the audio signal, transcribing note onsets and offsets. The system consists of three main stages: filtering, partial tracking and note extraction. The paper presents our partial tracking method based...
متن کاملSONIC: A System for Transcription of Piano Music
This paper presents our system for transcription of polyphonic piano music – SONIC. SONIC takes an audio signal of a piano performance and tries to determine which notes were played by the performer, thus producing a list of notes and note onset times approximately matching the performance. SONIC is composed of three main parts: partial extractor, onset detector and note recognizer. We present ...
متن کاملAutomatic Transcription of Polyphonic Piano Music Using a Note Masking Technique
This paper describes a polyphonic note detection system incorporating a simple masking technique that can accurately transcribe chords and polyphonic piano music. The system, developed in MATLAB, will take input files in .wav format. The music is segmented by using Note Average Energy (NAE) onset detection. Onsets are used to segment the music into note windows which are then analysed using the...
متن کامل