Signal Processing Methods for Music Transcription

By Anssi Klapuri, Manuel Davy. 2006

657 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0d871eaa34d764cc31a9b185f414da623d81b375

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In Artificial Intelligence

In Signal Processing

Citations in the corpus

Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics (2012, background): “[8, 22, 29]) amply document what is a decades-long research effort at the intersection of music, machine learning and signal processing, with wide applicability to a range of tasks including the automatic identification of melodies, chords, instrumentation, tempo, long-term structure, genre, artist,”
An End-to-End Neural Network for Polyphonic Piano Music Transcription (2015, background): “scription, given a polyphonic acoustic signal. Music transcription is considered to be a difﬁcult problem even by human experts and current music transcription systems fail to match human performance [1]. Polyphonic AMT is a difﬁcult problem because concurrently sounding notes from one or more instr”
Feature learning and deep architectures: new directions for music informatics (2013, background): “Along the way, it was also discovered that applying nonlinear compression to a novelty function produced noticeably better results (Klapuri and Davy 2006).”
Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle (2010, positive): “This leads to a complete generative spectral model for simultaneous piano notes, which also explicitly includes the typical deviation from exact harmonicity in a piano overtone series.”
Automatic music transcription: challenges and future directions (2013, background): “Even for expert musicians, transcribing polyphonic pieces of music is not a trivial task (see Chapter 1 of [75] and [77]), and while the problem of automatic pitch estimation for monophonic signals might be considered solved, the creation of an automated system able to transcribe polyphonic music wi”
A Survey of Audio-Based Music Classification and Annotation (2011, background): “been made in the first approach via explicit extraction of features such as rhythm, pitch, and harmony from audio signals based on the general knowledge and understanding of music [1], [104], [106], [129], [145].”