Recognition of Harmonic Sounds in Polyphonic Audio using a Missing Feature Approach: Extended Report

A method based on local spectral features and missing feature techniques is proposed for the recognition of harmonic sounds in mixture signals. A mask estimation algorithm is proposed for identifying spectral regions that contain reliable information for each sound source and then bounded marginalization is employed to treat the feature vector elements that are determined as unreliable. The proposed method is tested on musical instrument sounds due to the extensive availability of data but it can be applied on other sounds (i.e. animal sounds, environmental sounds), whenever these are harmonic. In simulations the proposed method clearly outperformed a baseline method for mixture signals

The Temperament Police: The Truth, the Ground Truth, and Nothing but the Truth

The tuning system of a keyboard instrument is chosen so that frequently used musical intervals sound as consonant as possible. Temperament refers to the compromise arising from the fact that not all intervals can be maximally consonant simultaneously. Recent work showed that it is possible to estimate temperament from audio recordings with no prior knowledge of the musical score, using a conservative (high precision, low recall) automatic transcription algorithm followed by frequency estimation using quadratic interpolation and bias correction from the log magnitude spectrum. In this paper we develop a harpsichord-specific transcription system to analyse over 500 recordings of solo harpsichord music for which the temperament is specified on the CD sleeve notes. We compare the measured temperaments with the annotations and discuss the differences between temperament as a theoretical construct and as a practical issue for professional performers and tuners. The implications are that ground truth is not always scientific truth, and that content-based analysis has an important role in the study of historical performance practice. 1

Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription

In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated

New tonalities with the Thummer and The Viking

In this paper we explain the theoretical background of Dynamic Tonality using the Thummer, a new musical interface, and The Viking, a software synthesizer written especially for it. Dynamic Tonality is a musical audio routine that allows for novel tunings and enables the user to relate – to an arbitrary degree – these tunings with the partials of their notes. The Viking features Dynamic Tonality and works with any MIDI instrument, but when paired with the Thummer (or another two-dimensional interface) it creates a system of fingering invariance across chords and tunings. Thus, the Thummer and The Viking render non-standard tunings more physically, pedagogically, and aesthetically accessible

Sound Source Separation

This is the author's accepted pre-print of the article, first published as G. Evangelista, S. Marchand, M. D. Plumbley and E. Vincent. Sound source separation. In U. Zölzer (ed.), DAFX: Digital Audio Effects, 2nd edition, Chapter 14, pp. 551-588. John Wiley & Sons, March 2011. ISBN 9781119991298. DOI: 10.1002/9781119991298.ch14file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.2

Lauluyhtyeen intonaation automaattinen määritys

The objective of this study is a specific music signal processing task, primarily intended to help vocal ensemble singers practice their intonation. In this case intonation is defined as deviations of pitch in relation to the note written in the score which are small, less than a semitone. These can be either intentional or unintentional. Practicing intonation is typically challenging without an external ear. The algorithm developed in this thesis combined with the presented application concept can act as the external ear, providing real-time information on intonation to support practicing. The method can be applied to the analysis of recorded material as well. The music signal generated by a vocal ensemble is polyphonic. It contains multiple simultaneous tones with partly or completely overlapping harmonic partials. We need to be able to estimate the fundamental frequency of each tone, which then indicates the pitch of each singer. Our experiments show, that the fundamental frequency estimation method based on the Fourier analysis developed in this thesis can be applied to the automatic analysis of vocal ensembles. A sufficient frequency resolution can be achieved without compromising the time resolution too much by using an adequately sized window. The accuracy and robustness can be further increased by taking advantage of solitary partials. The greatest challenge turned out to be the estimation of tones in octave and unison relationships. These intervals are fairly common in tonal music. This question requires further investigation or another type of approach.Tässä työssä tutkitaan erityistä musiikkisignaalin analysointitehtävää, jonka tarkoi- tuksena on auttaa lauluyhtyelaulajia intonaation harjoittelussa. Intonaatiolla tar- koitetaan tässä yhteydessä pieniä, alle puolen sävelaskeleen säveltasoeroja nuottiin kirjoitettuun sävelkorkeuteen nähden, jotka voivat olla joko tarkoituksenmukaisia tai tahattomia. Intonaation harjoittelu on tyypillisesti haastavaa ilman ulkopuolista korvaa. Työssä kehitetty algoritmi yhdessä esitellyn sovelluskonseptin kanssa voi toimia harjoittelutilanteessa ulkopuolisena korvana tarjoten reaaliaikaista tietoa intonaatiosta harjoittelun tueksi. Vaihtoehtoisesti menetelmää voidaan hyödyntää harjoitusäänitteiden analysointiin jälkikäteen. Lauluyhtyeen tuottama musiikki- signaali on polyfoninen. Se sisältää useita päällekkäisiä säveliä, joiden osasävelet menevät toistensa kanssa osittain tai kokonaan päällekkäin. Tästä signaalista on pystyttävä tunnistamaan kunkin sävelen perustaajuus, joka puolestaan kertoo lau- lajan laulaman sävelkorkeuden. Kokeellisten tulosten perusteella työssä kehitettyä Fourier-muunnokseen perustuvaa taajuusanalyysiä voidaan soveltaa lauluyhtyeen intonaation automaattiseen määritykseen, kun nuottiin kirjoitettua sointua hyödyn- netään analyysin lähtötietona. Sopivankokoista näyteikkunaa käyttämällä päästiin riittävään taajuusresoluutioon aikaresoluution säilyessä kohtuullisena. Yksinäisiä osasäveliä hyödyntämällä voidaan edelleen parantaa tarkkuutta ja toimintavar- muutta. Suurimmaksi haasteeksi osoittautui oktaavi- ja priimisuhteissa olevien intervallien luotettava määritys. Näitä intervallisuhteita esiintyy tonaalisessa musii- kissa erityisen paljon. Tämä kysymys vaatii vielä lisätutkimusta tai uudenlaista lähestymistapaa

A Novel Techniques for Classification of Musical Instruments

Musical instrument classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. Signal is subjected to wavelet decomposition. A suitable wavelet is selected for decomposition. In our work for decomposition we used Wavelet Packet transform. After the wavelet decomposition, some sub band signals can be analyzed, particular band can be representing the particular characteristics of musical signal. Finally these wavelet features set were formed and then musical instrument will be classified by using suitable machine learning algorithm (classifier). In this paper, the problem of classifying of musical instruments is addressed.  We propose a new musical instrument classification method based on wavelet represents both local and global information by computing wavelet coefficients at different frequency sub bands with different resolutions. Using wavelet packet transform (WPT) along with advanced machine learning techniques, accuracy of music instrument classification has been significantly improved. Keywords: Musical instrument classification, WPT, Feature Extraction Techniques, Machine learning techniques

Perceptually smooth timbral guides by state-space analysis of phase-vocoder parameters

Sculptor is a phase-vocoder-based package of programs that allows users to explore timbral manipulation of sound in real time. It is the product of a research program seeking ultimately to perform gestural capture by analysis of the sound a performer makes using a conventional instrument. Since the phase-vocoder output is of high dimensionality — typically more than 1,000 channels per analysis frame—mapping phase-vocoder output to appropriate input parameters for a synthesizer is only feasible in theory