audio extraction-technology


A Matlab toolbox for musical feature extraction from audio
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We present MIRtoolbox, an integrated set of functions written in Matlab, dedicated to the extraction of musical features from audio files. The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of

YAAFE, an Easy to Use and Efficient Audio Feature Extraction Software.
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ABSTRACT Music Information Retrieval systems are commonly built on a feature extraction stage. For applications involving automatic classification (eg speech/music discrimination, music genre or mood recognition,), traditional approaches will consider a large set of

Musical key extraction from audio .
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The realisation and evaluation of a musical key extraction algorithm that works directly on raw audio data is presented. Its implementation is based on models of human auditory perception and music cognition. It is straightforward and has minimal computing

Generation of sports highlights using motion activity in combination with a common audio feature extraction framework.
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In our past work we have used temporal patterns of motion activity to extract sports highlights. We have also used audio classification based approaches to develop a common audiobased platform for feature extraction that works across three different sports. In this

Libxtract: a Lightweight Library for audio Feature Extraction .
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The libxtract library consists of a collection of over forty functions that can be used for the extraction of low level audio features. In this paper I will describe the development and usage of the library as well as the rationale for its design. Its use in the composition and

Fusion of audio and motion information on HMM-based highlight extraction for baseball games
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This paper aims to extract baseball game highlights based on audio -motion integrated cues. In order to better describe different audio and motion characteristics in baseball game highlights, we propose a novel representation method based on likelihood models. The

SAFE: A system for the extraction and retrieval of semantic audio descriptors
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We present an overview of the Semantic Audio Feature Extraction (SAFE) Project, a novel data collection architecture for the extraction and retrieval of semantic descriptions of musical timbre, deployed within the digital audio workstation. By embedding the data

Musical Key Extraction from Audio Using Profile Training.
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A new method is presented for extracting the musical key from raw audio data. The method is based on the extraction of chromagrams using a new approach for tonal component selection taking into account auditory masking. The extracted chromagrams were used to

Harmonic-temporal structured clustering via deterministic annealing EM algorithm for audio feature extraction
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This paper proposes harmonic-temporal structured clustering (HTC) method , that allows simultaneous estimation of pitch, intensity, onset, duration, etc., of each underlying source in multi-stream audio signal, which we expect to be an effective feature extraction for MIR

Gaussian Mixture Models For Extraction Of Melodic Lines From Audio Recordings.
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The presented study deals with extraction of melodic line (s) from polyphonic audio recordings. We base our work on the use of expectation maximization algorithm, which is employed in a two-step procedure that finds melodic lines in audio signals. In the first step

Extraction of the melody pitch contour from polyphonic audio
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ABSTRACT MIREX 2005 is the second evaluation of algorithms related to music information retrieval (MIR). This document describes our submission to the MIREX audio melody extraction contest addressing the task of identifying the melody pitch contour from

Audio melody extraction for mirex 2009
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This paper describes our submission to the audio melody extraction evaluation addressing the task of identifying the melody pitch contour from polyphonic musical audio . It shall give an overview about the algorithm and a discussion of the evaluation results. The presented

Extraction of musical performance parameters from audio data
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We present a system for the automatic extraction of musical content from audio signals containing polyphonic music. The system works off-line, taking data from audio files and producing MIDI output, representing the pitch, timing and volume of the musical notes. The

An evaluation of audio feature extraction toolboxes
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Audio feature extraction underpins a massive proportion of audio processing, music information retrieval, audio effect design and audio synthesis. Design, analysis, synthesis and evaluation often rely on audio features, but there are a large and diverse range of

Feature extraction for the prediction of multichannel spatial audio fidelity
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This paper seeks to present an algorithm for the pre-diction of frontal spatial fidelity and surround spatial fidelity of multichannel audio , which are two attributes of the subjective parameter called basic audio quality. A number of features chosen to represent spectral and

A robust audio fingerprint extraction algorithm
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An Audio fingerprint is a small digest of an audio file computed from its main perceptual properties. Like human fingerprints, Audio fingerprints allows to identify an audio file among a set of candidates but does not allow to retreive any other characteristics of the files

A study on the frequency-domain Primary-ambient extraction for stereo audio signals.
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Primary-ambient extraction (PAE) has been playing an important role in spatial audio analysis-synthesis. Based on the spatial features, PAE decomposes a signal into primary and ambient components, which are then rendered separately. PAE is performed in

Recognising acoustic scenes with large-scale audio feature extraction and SVM
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This work describes our contribution to the IEEE AASP Challenge on classification of acoustic scenes. From the 30 second long highly variable recordings, spectral, cepstral, energy and voicing-related audio features are extracted. A sliding window approach is used

Audio melody extraction based on timbral similarity of melodic fragments
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The presented study deals with extraction of melodic line (s) from polyphonic audio recordings. Our approach is based on finding significant melodic fragments throughout the analyzed piece of music and clustering these fragments according to their timbral similarity

Audio Content Extraction from MPEG-encoded sequences
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Audio content analysis is nowadays mainly performed on quantised sound waves after transforming the samples into the frequency domain. Our work exploits MPEG-1 encoded audio data for audio content analysis. As MPEG-1 uses Subband Coding to compress sound





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