60 research outputs found
Efficient methods for joint estimation of multiple fundamental frequencies in music signals
This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.This study was supported by the project DRIMS (code TIN2009-14247-C02), the Consolider Ingenio 2010 research programme (project MIPRCV, CSD2007-00018), and the PASCAL2 Network of Excellence, IST-2007-216886
Automatic transcription of polyphonic music exploiting temporal evolution
PhDAutomatic music transcription is the process of converting an audio recording
into a symbolic representation using musical notation. It has numerous applications
in music information retrieval, computational musicology, and the
creation of interactive systems. Even for expert musicians, transcribing polyphonic
pieces of music is not a trivial task, and while the problem of automatic
pitch estimation for monophonic signals is considered to be solved, the creation
of an automated system able to transcribe polyphonic music without setting
restrictions on the degree of polyphony and the instrument type still remains
open.
In this thesis, research on automatic transcription is performed by explicitly
incorporating information on the temporal evolution of sounds. First efforts address
the problem by focusing on signal processing techniques and by proposing
audio features utilising temporal characteristics. Techniques for note onset and
offset detection are also utilised for improving transcription performance. Subsequent
approaches propose transcription models based on shift-invariant probabilistic
latent component analysis (SI-PLCA), modeling the temporal evolution
of notes in a multiple-instrument case and supporting frequency modulations in
produced notes. Datasets and annotations for transcription research have also
been created during this work. Proposed systems have been privately as well as
publicly evaluated within the Music Information Retrieval Evaluation eXchange
(MIREX) framework. Proposed systems have been shown to outperform several
state-of-the-art transcription approaches.
Developed techniques have also been employed for other tasks related to music
technology, such as for key modulation detection, temperament estimation,
and automatic piano tutoring. Finally, proposed music transcription models
have also been utilized in a wider context, namely for modeling acoustic scenes
Towards the automated analysis of simple polyphonic music : a knowledge-based approach
PhDMusic understanding is a process closely related to the knowledge and experience
of the listener. The amount of knowledge required is relative to the
complexity of the task in hand.
This dissertation is concerned with the problem of automatically decomposing
musical signals into a score-like representation. It proposes that, as
with humans, an automatic system requires knowledge about the signal and
its expected behaviour to correctly analyse music.
The proposed system uses the blackboard architecture to combine the
use of knowledge with data provided by the bottom-up processing of the
signal's information. Methods are proposed for the estimation of pitches,
onset times and durations of notes in simple polyphonic music.
A method for onset detection is presented. It provides an alternative to
conventional energy-based algorithms by using phase information. Statistical
analysis is used to create a detection function that evaluates the expected
behaviour of the signal regarding onsets.
Two methods for multi-pitch estimation are introduced. The first concentrates
on the grouping of harmonic information in the frequency-domain.
Its performance and limitations emphasise the case for the use of high-level
knowledge.
This knowledge, in the form of the individual waveforms of a single
instrument, is used in the second proposed approach. The method is based
on a time-domain linear additive model and it presents an alternative to
common frequency-domain approaches.
Results are presented and discussed for all methods, showing that, if
reliably generated, the use of knowledge can significantly improve the quality
of the analysis.Joint Information Systems Committee (JISC) in the UK National Science Foundation (N.S.F.) in the United states. Fundacion Gran Mariscal Ayacucho in Venezuela
Automatic Drum Transcription and Source Separation
While research has been carried out on automated polyphonic music transcription, to-date the problem of automated polyphonic percussion transcription has not received the same degree of attention. A related problem is that of sound source separation, which attempts to separate a mixture signal into its constituent sources. This thesis focuses on the task of polyphonic percussion transcription and sound source separation of a limited set of drum instruments, namely the drums found in the standard rock/pop drum kit. As there was little previous research on polyphonic percussion transcription a broad review of music information retrieval methods, including previous polyphonic percussion systems, was also carried out to determine if there were any methods which were of potential use in the area of polyphonic drum transcription. Following on from this a review was conducted of general source separation and redundancy reduction techniques, such as Independent Component Analysis and Independent Subspace Analysis, as these techniques have shown potential in separating mixtures of sources. Upon completion of the review it was decided that a combination of the blind separation approach, Independent Subspace Analysis (ISA), with the use of prior knowledge as used in music information retrieval methods, was the best approach to tackling the problem of polyphonic percussion transcription as well as that of sound source separation. A number of new algorithms which combine the use of prior knowledge with the source separation abilities of techniques such as ISA are presented. These include sub-band ISA, Prior Subspace Analysis (PSA), and an automatic modelling and grouping technique which is used in conjunction with PSA to perform polyphonic percussion transcription. These approaches are demonstrated to be effective in the task of polyphonic percussion transcription, and PSA is also demonstrated to be capable of transcribing drums in the presence of pitched instruments
High-resolution sinusoidal analysis for resolving harmonic collisions in music audio signal processing
Many music signals can largely be considered an additive combination of
multiple sources, such as musical instruments or voice. If the musical sources
are pitched instruments, the spectra they produce are predominantly harmonic,
and are thus well suited to an additive sinusoidal model. However,
due to resolution limits inherent in time-frequency analyses, when the harmonics
of multiple sources occupy equivalent time-frequency regions, their
individual properties are additively combined in the time-frequency representation
of the mixed signal. Any such time-frequency point in a mixture
where multiple harmonics overlap produces a single observation from which
the contributions owed to each of the individual harmonics cannot be trivially
deduced. These overlaps are referred to as overlapping partials or harmonic
collisions. If one wishes to infer some information about individual sources in
music mixtures, the information carried in regions where collided harmonics
exist becomes unreliable due to interference from other sources. This interference
has ramifications in a variety of music signal processing applications
such as multiple fundamental frequency estimation, source separation, and
instrumentation identification.
This thesis addresses harmonic collisions in music signal processing applications.
As a solution to the harmonic collision problem, a class of signal
subspace-based high-resolution sinusoidal parameter estimators is explored.
Specifically, the direct matrix pencil method, or equivalently, the Estimation
of Signal Parameters via Rotational Invariance Techniques (ESPRIT)
method, is used with the goal of producing estimates of the salient parameters
of individual harmonics that occupy equivalent time-frequency regions. This
estimation method is adapted here to be applicable to time-varying signals
such as musical audio. While high-resolution methods have been previously
explored in the context of music signal processing, previous work has not
addressed whether or not such methods truly produce high-resolution sinusoidal parameter estimates in real-world music audio signals. Therefore, this
thesis answers the question of whether high-resolution sinusoidal parameter
estimators are really high-resolution for real music signals.
This work directly explores the capabilities of this form of sinusoidal parameter
estimation to resolve collided harmonics. The capabilities of this
analysis method are also explored in the context of music signal processing
applications. Potential benefits of high-resolution sinusoidal analysis are
examined in experiments involving multiple fundamental frequency estimation
and audio source separation. This work shows that there are indeed
benefits to high-resolution sinusoidal analysis in music signal processing applications,
especially when compared to methods that produce sinusoidal
parameter estimates based on more traditional time-frequency representations.
The benefits of this form of sinusoidal analysis are made most evident
in multiple fundamental frequency estimation applications, where substantial
performance gains are seen. High-resolution analysis in the context of
computational auditory scene analysis-based source separation shows similar
performance to existing comparable methods
Pitch-Informed Solo and Accompaniment Separation
Das Thema dieser Dissertation ist die Entwicklung eines Systems zur
Tonhöhen-informierten Quellentrennung von Musiksignalen in Soloinstrument
und Begleitung. Dieses ist geeignet, die dominanten Instrumente aus einem
Musikstück zu isolieren, unabhängig von der Art des Instruments, der
Begleitung und Stilrichtung. Dabei werden nur einstimmige
Melodieinstrumente in Betracht gezogen. Die Musikaufnahmen liegen monaural
vor, es kann also keine zusätzliche Information aus der Verteilung der
Instrumente im Stereo-Panorama gewonnen werden.
Die entwickelte Methode nutzt Tonhöhen-Information als Basis für eine
sinusoidale Modellierung der spektralen Eigenschaften des Soloinstruments
aus dem Musikmischsignal. Anstatt die spektralen Informationen pro Frame zu
bestimmen, werden in der vorgeschlagenen Methode Tonobjekte für die
Separation genutzt. Tonobjekt-basierte Verarbeitung ermöglicht es,
zusätzlich die Notenanfänge zu verfeinern, transiente Artefakte zu
reduzieren, gemeinsame Amplitudenmodulation (Common Amplitude Modulation
CAM) einzubeziehen und besser nichtharmonische Elemente der Töne
abzuschätzen. Der vorgestellte Algorithmus zur Quellentrennung von
Soloinstrument und Begleitung ermöglicht eine Echtzeitverarbeitung und ist
somit relevant für den praktischen Einsatz.
Ein Experiment zur besseren Modellierung der Zusammenhänge zwischen
Magnitude, Phase und Feinfrequenz von isolierten Instrumententönen wurde
durchgeführt. Als Ergebnis konnte die Kontinuität der zeitlichen
Einhüllenden, die Inharmonizität bestimmter Musikinstrumente und die
Auswertung des Phasenfortschritts für die vorgestellte Methode ausgenutzt
werden. Zusätzlich wurde ein Algorithmus für die Quellentrennung in
perkussive und harmonische Signalanteile auf Basis des Phasenfortschritts
entwickelt. Dieser erreicht ein verbesserte perzeptuelle Qualität der
harmonischen und perkussiven Signale gegenüber vergleichbaren Methoden nach
dem Stand der Technik.
Die vorgestellte Methode zur Klangquellentrennung in Soloinstrument und
Begleitung wurde zu den Evaluationskampagnen SiSEC 2011 und SiSEC 2013
eingereicht. Dort konnten vergleichbare Ergebnisse im Hinblick auf
perzeptuelle Bewertungsmaße erzielt werden. Die Qualität eines
Referenzalgorithmus im Hinblick auf den in dieser Dissertation
beschriebenen Instrumentaldatensatz übertroffen werden.
Als ein Anwendungsszenario für die Klangquellentrennung in Solo und
Begleitung wurde ein Hörtest durchgeführt, der die Qualitätsanforderungen
an Quellentrennung im Kontext von Musiklernsoftware bewerten sollte. Die
Ergebnisse dieses Hörtests zeigen, dass die Solo- und Begleitspur gemäß
unterschiedlicher Qualitätskriterien getrennt werden sollten. Die
Musiklernsoftware Songs2See integriert die vorgestellte
Klangquellentrennung bereits in einer kommerziell erhältlichen Anwendung.This thesis addresses the development of a system for pitch-informed solo
and accompaniment separation capable of separating main instruments from
music accompaniment regardless of the musical genre of the track, or type
of music accompaniment. For the solo instrument, only pitched monophonic
instruments were considered in a single-channel scenario where no panning
or spatial location information is available.
In the proposed method, pitch information is used as an initial stage of a
sinusoidal modeling approach that attempts to estimate the spectral
information of the solo instrument from a given audio mixture. Instead of
estimating the solo instrument on a frame by frame basis, the proposed
method gathers information of tone objects to perform separation.
Tone-based processing allowed the inclusion of novel processing stages for
attack refinement, transient interference reduction, common amplitude
modulation (CAM) of tone objects, and for better estimation of non-harmonic
elements that can occur in musical instrument tones. The proposed solo and
accompaniment algorithm is an efficient method suitable for real-world
applications.
A study was conducted to better model magnitude, frequency, and phase of
isolated musical instrument tones. As a result of this study, temporal
envelope smoothness, inharmonicty of musical instruments, and phase
expectation were exploited in the proposed separation method. Additionally,
an algorithm for harmonic/percussive separation based on phase expectation
was proposed. The algorithm shows improved perceptual quality with respect
to state-of-the-art methods for harmonic/percussive separation.
The proposed solo and accompaniment method obtained perceptual quality
scores comparable to other state-of-the-art algorithms under the SiSEC 2011
and SiSEC 2013 campaigns, and outperformed the comparison algorithm on the
instrumental dataset described in this thesis.As a use-case of solo and
accompaniment separation, a listening test procedure was conducted to
assess separation quality requirements in the context of music education.
Results from the listening test showed that solo and accompaniment tracks
should be optimized differently to suit quality requirements of music
education. The Songs2See application was presented as commercial music
learning software which includes the proposed solo and accompaniment
separation method
Audio source separation techniques including novel time-frequency representation tools
The thesis explores the development of tools for audio representation with applications in Audio Source Separation and in the Music Information Retrieval (MIR) field. A novel constant Q transform was introduced, called IIR-CQT. The transform allows a flexible design and achieves low computational cost. Also, an independent development of the Fan Chirp Transform (FChT) with the focus on the representation of simultaneous sources is studied, which has several applications in the analysis of polyphonic music signals. Dierent applications are explored in the MIR field, some of them directly related with the low-level representation tools that were analyzed. One of these applications is the development of a visualization tool based in the FChT that proved to be useful for musicological analysis . The tool has been made available as an open source, freely available software. The proposed Transform has also been used to detect and track fundamental frequencies of harmonic sources in polyphonic music. Also, the information of the slope of the pitch was used to define a similarity measure between two harmonic components that are close in time. This measure helps to use clustering algorithms to track multiple sources in polyphonic music. Additionally, the FChT was used in the context of the Query by Humming application. One of the main limitations of such application is the construction of a search database. In this work, we propose an algorithm to automatically populate the database of an existing Query by Humming, with promising results. Finally, two audio source separation techniques are studied. The first one is the separation of harmonic signals based on the FChT. The second one is an application for which the fundamental frequency of the sources is assumed to be known (Score Informed Source Separation problem)
Contributions to automatic multiple F0 detection in polyphonic music signals
Multiple fundamental frequency estimation, or multi-pitch estimation (MPE), is a key problem in automatic music transcription (AMT) and many other related audio processing tasks. Applications of AMT are numerous, ranging from musical genre classification to automatic piano tutoring, and these form a significant part of musical information retrieval tasks. Current AMT systems still perform considerably below human experts, and there is a consensus that the development of an automated system for full transcription of polyphonic music regardless of its complexity is still an open problem. The goal of this work is to propose contributions for the automatic detection of multiple fundamental frequencies in polyphonic music signals. A reference MPE method is chosen to be studied and implemented, and a modification is proposed to improve the performance of the system. Lastly, three refinement strategies are proposed to be incorporated into the modified method, in order to increase the quality of the results. Experimental tests reveal that such refinements improve the overall performance of the system, even if each one performs differently according to signal characteristics.Estimação de múltiplas frequências fundamentais (MPE, do inglês multipitch estimation) é um problema importante na área de transcrição musical automática (TMA) e em muitas outras tarefas relacionadas a processamento de áudio. Aplicações de TMA são diversas, desde classificação de gêneros musicais ao aprendizado automático de piano, as quais consistem em uma parcela significativa de tarefas de extração de informação musical. Métodos atuais de TMA ainda possuem um desempenho consideravelmente ruim quando comparados aos de profissionais da área, e há um consenso que o desenvolvimento de um sistema automatizado para a transcrição completa de música polifônica independentemente de sua complexidade ainda é um problema em aberto. O objetivo deste trabalho é propor contribuições para a detecção automática de múltiplas frequências fundamentais em sinais de música polifônica. Um método de referência para MPEé primeiramente escolhido para ser estudado e implementado, e uma modificação é proposta para melhorar o desempenho do sistema. Por fim, três estratégias de refinamento são propostas para serem incorporadas ao método modificado, com o objetivo de aumentar a qualidade dos resultados. Testes experimentais mostram que tais refinamentos melhoram em média o desempenho do sistema, embora cada um atue de uma maneira diferente de acordo com a natureza dos sinais
Iterative Separation of Note Events from Single-Channel Polyphonic Recordings
This thesis is concerned with the separation of audio sources from single-channel polyphonic musical recordings using the iterative estimation and separation of note events. Each event is defined as a section of audio containing largely harmonic energy identified as coming from a single sound source. Multiple events can be clustered to form separated sources. This solution is a model-based algorithm that can be applied to a large variety of audio recordings without requiring previous training stages.
The proposed system embraces two principal stages. The first one considers the iterative detection and separation of note events from within the input mixture. In every iteration, the pitch trajectory of the predominant note event is automatically selected from an array of fundamental frequency estimates and used to guide the separation of the event's spectral content using two different methods: time-frequency masking and time-domain subtraction. A residual signal is then generated and used as the input mixture for the next iteration. After convergence, the second stage considers the clustering of all detected note events into individual audio sources.
Performance evaluation is carried out at three different levels. Firstly, the accuracy of the note-event-based multipitch estimator is compared with that of the baseline algorithm used in every iteration to generate the initial set of pitch estimates. Secondly, the performance of the semi-supervised source separation process is compared with that of another semi-automatic algorithm. Finally, a listening test is conducted to assess the audio quality and naturalness of the separated sources when they are used to create stereo mixes from monaural recordings.
Future directions for this research focus on the application of the proposed system to other music-related tasks. Also, a preliminary optimisation-based approach is presented as an alternative method for the separation of overlapping partials, and as a high resolution time-frequency representation for digital signals
- …