1,050 research outputs found
Iracema: a Python library for audio content analysis
Iracema is a Python library that aims to provide models for the extraction of meaningful informationfrom recordings of monophonic pieces of music, for purposes of research in music performance. With this objective in mind, we propose an architecture that will provide to users an abstraction level that simplifies the manipulation of different kinds of time series, as well as the extraction of segments from them. In this paper we: (1) introduce some key concepts at the core of the proposed architecture; (2) describe the current functionalities of the package; (3) give some examples of the application programming interface; and (4) give some brief examples of audio analysis using the system
A new approach to onset detection: towards an empirical grounding of theoretical and speculative ideologies of musical performance
This article assesses aspects of the current state of a project which aims, with the help of computers
and computer software, to segment soundfiles of vocal melodies into their component notes, identifying
precisely when the onset of each note occurs, and then tracking the pitch trajectory of each
note, especially in melodies employing a variety of non-standard temperaments, in which musical
intervals smaller than 100 cents are ubiquitous. From there, we may proceed further, to describe
many other “micro-features” of each of the notes, but for now our focus is on the onset times and
pitch trajectories
Onset Event Decoding Exploiting the Rhythmic Structure of Polyphonic Music
(c)2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published version: IEEE Journal of Selected Topics in Signal Processing 5(6): 1228-1239, Oct 2011. DOI:10.1109/JSTSP.2011.214622
Extracting expressive performance information from recorded music
Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.Includes bibliographical references (leaves 55-56).by Eric David Scheirer.M.S
Deep Polyphonic ADSR Piano Note Transcription
We investigate a late-fusion approach to piano transcription, combined with a
strong temporal prior in the form of a handcrafted Hidden Markov Model (HMM).
The network architecture under consideration is compact in terms of its number
of parameters and easy to train with gradient descent. The network outputs are
fused over time in the final stage to obtain note segmentations, with an HMM
whose transition probabilities are chosen based on a model of attack, decay,
sustain, release (ADSR) envelopes, commonly used for sound synthesis. The note
segments are then subject to a final binary decision rule to reject too weak
note segment hypotheses. We obtain state-of-the-art results on the MAPS
dataset, and are able to outperform other approaches by a large margin, when
predicting complete note regions from onsets to offsets.Comment: 5 pages, 2 figures, published as ICASSP'1
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