Multiple-F0 estimation of piano sounds exploiting spectral structure and temporal evolution

This paper proposes a system for multiple fundamental frequency estimation of piano sounds using pitch candidate selection rules which employ spectral structure and temporal evolution. As a time-frequency representation, the Resonator Time-Frequency Image of the input signal is employed, a noise suppression model is used, and a spectral whitening procedure is performed. In addition, a spectral flux-based onset detector is employed in order to select the steady-state region of the produced sound. In the multiple-F0 estimation stage, tuning and inharmonicity parameters are extracted and a pitch salience function is proposed. Pitch presence tests are performed utilizing information from the spectral structure of pitch candidates, aiming to suppress errors occurring at multiples and sub-multiples of the true pitches. A novel feature for the estimation of harmonically related pitches is proposed, based on the common amplitude modulation assumption. Experiments are performed on the MAPS database using 8784 piano samples of classical, jazz, and random chords with polyphony levels between 1 and 6. The proposed system is computationally inexpensive, being able to perform multiple-F0 estimation experiments in realtime. Experimental results indicate that the proposed system outperforms state-of-the-art approaches for the aforementioned task in a statistically significant manner. Index Terms: multiple-F0 estimation, resonator timefrequency image, common amplitude modulatio

Audio source separation for music in low-latency and high-latency scenarios

Aquesta tesi proposa mètodes per tractar les limitacions de les tècniques existents de separació de fonts musicals en condicions de baixa i alta latència. En primer lloc, ens centrem en els mètodes amb un baix cost computacional i baixa latència. Proposem l'ús de la regularització de Tikhonov com a mètode de descomposició de l'espectre en el context de baixa latència. El comparem amb les tècniques existents en tasques d'estimació i seguiment dels tons, que són passos crucials en molts mètodes de separació. A continuació utilitzem i avaluem el mètode de descomposició de l'espectre en tasques de separació de veu cantada, baix i percussió. En segon lloc, proposem diversos mètodes d'alta latència que milloren la separació de la veu cantada, gràcies al modelatge de components específics, com la respiració i les consonants. Finalment, explorem l'ús de correlacions temporals i anotacions manuals per millorar la separació dels instruments de percussió i dels senyals musicals polifònics complexes.Esta tesis propone métodos para tratar las limitaciones de las técnicas existentes de separación de fuentes musicales en condiciones de baja y alta latencia. En primer lugar, nos centramos en los métodos con un bajo coste computacional y baja latencia. Proponemos el uso de la regularización de Tikhonov como método de descomposición del espectro en el contexto de baja latencia. Lo comparamos con las técnicas existentes en tareas de estimación y seguimiento de los tonos, que son pasos cruciales en muchos métodos de separación. A continuación utilizamos y evaluamos el método de descomposición del espectro en tareas de separación de voz cantada, bajo y percusión. En segundo lugar, proponemos varios métodos de alta latencia que mejoran la separación de la voz cantada, gracias al modelado de componentes que a menudo no se toman en cuenta, como la respiración y las consonantes. Finalmente, exploramos el uso de correlaciones temporales y anotaciones manuales para mejorar la separación de los instrumentos de percusión y señales musicales polifónicas complejas.This thesis proposes specific methods to address the limitations of current music source separation methods in low-latency and high-latency scenarios. First, we focus on methods with low computational cost and low latency. We propose the use of Tikhonov regularization as a method for spectrum decomposition in the low-latency context. We compare it to existing techniques in pitch estimation and tracking tasks, crucial steps in many separation methods. We then use the proposed spectrum decomposition method in low-latency separation tasks targeting singing voice, bass and drums. Second, we propose several high-latency methods that improve the separation of singing voice by modeling components that are often not accounted for, such as breathiness and consonants. Finally, we explore using temporal correlations and human annotations to enhance the separation of drums and complex polyphonic music signals

Acoustically Inspired Probabilistic Time-domain Music Transcription and Source Separation.

PhD ThesisAutomatic music transcription (AMT) and source separation are important computational tasks, which can help to understand, analyse and process music recordings. The main purpose of AMT is to estimate, from an observed audio recording, a latent symbolic representation of a piece of music (piano-roll). In this sense, in AMT the duration and location of every note played is reconstructed from a mixture recording. The related task of source separation aims to estimate the latent functions or source signals that were mixed together in an audio recording. This task requires not only the duration and location of every event present in the mixture, but also the reconstruction of the waveform of all the individual sounds. Most methods for AMT and source separation rely on the magnitude of time-frequency representations of the analysed recording, i.e., spectrograms, and often arbitrarily discard phase information. On one hand, this decreases the time resolution in AMT. On the other hand, discarding phase information corrupts the reconstruction in source separation, because the phase of each source-spectrogram must be approximated. There is thus a need for models that circumvent phase approximation, while operating at sample-rate resolution. This thesis intends to solve AMT and source separation together from an unified perspective. For this purpose, Bayesian non-parametric signal processing, covariance kernels designed for audio, and scalable variational inference are integrated to form efficient and acoustically-inspired probabilistic models. To circumvent phase approximation while keeping sample-rate resolution, AMT and source separation are addressed from a Bayesian time-domain viewpoint. That is, the posterior distribution over the waveform of each sound event in the mixture is computed directly from the observed data. For this purpose, Gaussian processes (GPs) are used to define priors over the sources/pitches. GPs are probability distributions over functions, and its kernel or covariance determines the properties of the functions sampled from a GP. Finally, the GP priors and the available data (mixture recording) are combined using Bayes' theorem in order to compute the posterior distributions over the sources/pitches. Although the proposed paradigm is elegant, it introduces two main challenges. First, as mentioned before, the kernel of the GP priors determines the properties of each source/pitch function, that is, its smoothness, stationariness, and more importantly its spectrum. Consequently, the proposed model requires the design of flexible kernels, able to learn the rich frequency content and intricate properties of audio sources. To this end, spectral mixture (SM) kernels are studied, and the Mat ern spectral mixture (MSM) kernel is introduced, i.e. a modified version of the SM covariance function. The MSM kernel introduces less strong smoothness, thus it is more suitable for modelling physical processes. Second, the computational complexity of GP inference scales cubically with the number of audio samples. Therefore, the application of GP models to large audio signals becomes intractable. To overcome this limitation, variational inference is used to make the proposed model scalable and suitable for signals in the order of hundreds of thousands of data points. The integration of GP priors, kernels intended for audio, and variational inference could enable AMT and source separation time-domain methods to reconstruct sources and transcribe music in an efficient and informed manner. In addition, AMT and source separation are current challenges, because the spectra of the sources/pitches overlap with each other in intricate ways. Thus, the development of probabilistic models capable of differentiating sources/pitches in the time domain, despite the high similarity between their spectra, opens the possibility to take a step towards solving source separation and automatic music transcription. We demonstrate the utility of our methods using real and synthesized music audio datasets for various types of musical instruments

Statistical models for natural sounds

It is important to understand the rich structure of natural sounds in order to solve important tasks, like automatic speech recognition, and to understand auditory processing in the brain. This thesis takes a step in this direction by characterising the statistics of simple natural sounds. We focus on the statistics because perception often appears to depend on them, rather than on the raw waveform. For example the perception of auditory textures, like running water, wind, fire and rain, depends on summary-statistics, like the rate of falling rain droplets, rather than on the exact details of the physical source. In order to analyse the statistics of sounds accurately it is necessary to improve a number of traditional signal processing methods, including those for amplitude demodulation, time-frequency analysis, and sub-band demodulation. These estimation tasks are ill-posed and therefore it is natural to treat them as Bayesian inference problems. The new probabilistic versions of these methods have several advantages. For example, they perform more accurately on natural signals and are more robust to noise, they can also fill-in missing sections of data, and provide error-bars. Furthermore, free-parameters can be learned from the signal. Using these new algorithms we demonstrate that the energy, sparsity, modulation depth and modulation time-scale in each sub-band of a signal are critical statistics, together with the dependencies between the sub-band modulators. In order to validate this claim, a model containing co-modulated coloured noise carriers is shown to be capable of generating a range of realistic sounding auditory textures. Finally, we explored the connection between the statistics of natural sounds and perception. We demonstrate that inference in the model for auditory textures qualitatively replicates the primitive grouping rules that listeners use to understand simple acoustic scenes. This suggests that the auditory system is optimised for the statistics of natural sounds

일반화된 디리클레 사전확률을 이용한 비지도적 음원 분리 방법

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부, 2018. 2. 이교구.Music source separation aims to extract and reconstruct individual instrument sounds that constitute a mixture sound. It has received a great deal of attention recently due to its importance in the audio signal processing. In addition to its stand-alone applications such as noise reduction and instrument-wise equalization, the source separation can directly affect the performance of the various music information retrieval algorithms when used as a pre-processing. However, conventional source separation algorithms have failed to show satisfactory performance especially without the aid of spatial or musical information about the target source. To deal with this problem, we have focused on the spectral and temporal characteristics of sounds that can be observed in the spectrogram. Spectrogram decomposition is a commonly used technique to exploit such characteristicshowever, only a few simple characteristics such as sparsity were utilizable so far because most of the characteristics were difficult to be expressed in the form of algorithms. The main goal of this thesis is to investigate the possibility of using generalized Dirichlet prior to constrain spectral/temporal bases of the spectrogram decomposition algorithms. As the generalized Dirichlet prior is not only simple but also flexible in its usage, it enables us to utilize more characteristics in the spectrogram decomposition frameworks. From harmonic-percussive sound separation to harmonic instrument sound separation, we apply the generalized Dirichlet prior to various tasks and verify its flexible usage as well as fine performance.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Task of interest 4 1.2.1 Number of channels 4 1.2.2 Utilization of side-information 5 1.3 Approach 6 1.3.1 Spectrogram decomposition with constraints 7 1.3.2 Dirichlet prior 11 1.3.3 Contribution 12 1.4 Outline of the thesis 13 Chapter 2 Theoretical background 17 2.1 Probabilistic latent component analysis 18 2.2 Non-negative matrix factorization 21 2.3 Dirichlet prior 23 2.3.1 PLCA framework 24 2.3.2 NMF framework 26 2.4 Summary 28 Chapter 3 Harmonic-Percussive Source Separation Using Harmonicity and Sparsity Constraints . . 30 3.1 Introduction 30 3.2 Proposed method 33 3.2.1 Formulation of Harmonic-Percussive Separation 33 3.2.2 Relation to Dirichlet Prior 35 3.3 Performance evaluation 37 3.3.1 Sample Problem 37 3.3.2 Qualitative Analysis 38 3.3.3 Quantitative Analysis 42 3.4 Summary 43 Chapter 4 Exploiting Continuity/Discontinuity of Basis Vectors in Spectrogram Decomposition for Harmonic-Percussive Sound Separation 46 4.1 Introduction 46 4.2 Proposed Method 51 4.2.1 Characteristics of harmonic and percussive components 51 4.2.2 Derivation of the proposed method 56 4.2.3 Algorithm interpretation 61 4.3 Performance Evaluation 62 4.3.1 Parameter setting 63 4.3.2 Toy examples 66 4.3.3 SiSEC 2015 dataset 69 4.3.4 QUASI dataset 84 4.3.5 Subjective performance evaluation 85 4.3.6 Audio demo 87 4.4 Summary 87 Chapter 5 Informed Approach to Harmonic Instrument sound Separation 89 5.1 Introduction 89 5.2 Proposed method 91 5.2.1 Excitation-filter model 92 5.2.2 Linear predictive coding 94 5.2.3 Spectrogram decomposition procedure 96 5.3 Performance evaluation 99 5.3.1 Experimental settings 99 5.3.2 Performance comparison 101 5.3.3 Envelope extraction 102 5.4 Summary 104 Chapter 6 Blind Approach to Harmonic Instrument sound Separation 105 6.1 Introduction 105 6.2 Proposed method 106 6.3 Performance evaluation 109 6.3.1 Weight optimization 109 6.3.2 Performance comparison 109 6.3.3 Effect of envelope similarity 112 6.4 Summary 114 Chapter 7 Conclusion and Future Work 115 7.1 Contributions 115 7.2 Future work 119 7.2.1 Application to multi-channel audio environment 119 7.2.2 Application to vocal separation 119 7.2.3 Application to various audio source separation tasks 120 Bibliography 121 초 록 137Docto

Automated generation of movie tributes

O objetivo desta tese é gerar um tributo a um filme sob a forma de videoclip, considerando como entrada um filme e um segmento musical coerente. Um tributo é considerado um vídeo que contém os clips mais significativos de um filme, reproduzidos sequencialmente, enquanto uma música toca. Nesta proposta, os clips a constar do tributo final são o resultado da sumarização das legendas do filme com um algoritmo de sumarização genérico. É importante que o artefacto seja coerente e fluido, pelo que há a necessidade de haver um equilíbrio entre a seleção de conteúdo importante e a seleção de conteúdo que esteja em harmonia com a música. Para tal, os clips são filtrados de forma a garantir que apenas aqueles que contêm a mesma emoção da música aparecem no vídeo final. Tal é feito através da extração de vetores de características áudio relacionadas com emoções das cenas às quais os clips pertencem e da música, e, de seguida, da sua comparação por meio do cálculo de uma medida de distância. Por fim, os clips filtrados preenchem a música cronologicamente. Os resultados foram positivos: em média, os tributos produzidos obtiveram 7 pontos, numa escala de 0 a 10, em critérios como seleção de conteúdo e coerência emocional, fruto de avaliação humana.This thesis’ purpose is to generate a movie tribute in the form of a videoclip for a given movie and music. A tribute is considered to be a video containing meaningful clips from the movie playing along with a cohesive music piece. In this work, we collect the clips by summarizing the movie subtitles with a generic summarization algorithm. It is important that the artifact is coherent and fluid, hence there is the need to balance between the selection of important content and the selection of content that is in harmony with the music. To achieve so, clips are filtered so as to ensure that only those that contain the same emotion as the music are chosen to appear in the final video. This is made by extracting vectors of emotion-related audio features from the scenes they belong to and from the music, and then comparing them with a distance measure. Finally, filtered clips fill the music length in a chronological order. Results were positive: on average, the produced tributes obtained scores of 7, on a scale from 0 to 10, on content selection, and emotional coherence criteria, from human evaluation

Exploiting Piano Acoustics in Automatic Transcription

This work was supported by a joint Queen Mary/China Scholarship Council Scholarship.This work was supported by a joint Queen Mary/China Scholarship Council Scholarship.This work was supported by a joint Queen Mary/China Scholarship Council Scholarship.This work was supported by a joint Queen Mary/China Scholarship Council Scholarship.In this thesis we exploit piano acoustics to automatically transcribe piano recordings into a symbolic representation: the pitch and timing of each detected note. To do so we use approaches based on non-negative matrix factorisation (NMF). To motivate the main contributions of this thesis, we provide two preparatory studies: a study of using a deterministic annealing EM algorithm in a matrix factorisation-based system, and a study of decay patterns of partials in real-word piano tones. Based on these studies, we propose two generative NMF-based models which explicitly model different piano acoustical features. The first is an attack/decay model, that takes into account the time-varying timbre and decaying energy of piano sounds. The system divides a piano note into percussive attack and harmonic decay stages, and separately models the two parts using two sets of templates and amplitude envelopes. The two parts are coupled by the note activations. We simplify the decay envelope by an exponentially decaying function. The proposed method improves the performance of supervised piano transcription. The second model aims at using the spectral width of partials as an independent indicator of the duration of piano notes. Each partial is represented by a Gaussian function, with the spectral width indicated by the standard deviation. The spectral width is large in the attack part, but gradually decreases to a stable value and remains constant in the decay part. The model provides a new aspect to understand the time-varying timbre of piano notes, but furtherinvestigation is needed to use it effectively to improve piano transcription. We demonstrate the utility of the proposed systems in piano music transcription and analysis. Results show that explicitly modelling piano acoustical features, especially temporal features, can improve the transcription performance.Queen Mary/China Scholarship Council Scholarship

Generative rhythmic models

A system for generative rhythmic modeling is presented. The work aims to explore computational models of creativity, realizing them in a system designed for realtime generation of semi-improvisational music. This is envisioned as an attempt to develop musical intelligence in the context of structured improvisation, and by doing so to enable and encourage new forms of musical control and performance; the systems described in this work, already capable of realtime creation, have been designed with the explicit intention of embedding them in a variety of performance-based systems. A model of qaida, a solo tabla form, is presented, along with the results of an online survey comparing it to a professional tabla player's recording on dimensions of musicality, creativity, and novelty. The qaida model generates a bank of rhythmic variations by reordering subphrases. Selections from this bank are sequenced using a feature-based approach. An experimental extension into modeling layer- and loop-based forms of electronic music is presented, in which the initial modeling approach is generalized. Starting from a seed track, the layer-based model utilizes audio analysis techniques such as blind source separation and onset-based segmentation to generate layers which are shuffled and recombined to generate novel music in a manner analogous to the qaida model.M.S.Committee Chair: Chordia, Parag; Committee Member: Freeman, Jason; Committee Member: Weinberg, Gi

State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing

Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an $L^2$ regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intracortical sources. In our model, the observation model is derived from the steady-state solution to Maxwell's equations while the latent model representing neural dynamics is given by a random walk process.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS483 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org