From heuristics-based to data-driven audio melody extraction

The identification of the melody from a music recording is a relatively easy task for humans, but very challenging for computational systems. This task is known as "audio melody extraction", more formally defined as the automatic estimation of the pitch sequence of the melody directly from the audio signal of a polyphonic music recording. This thesis investigates the benefits of exploiting knowledge automatically derived from data for audio melody extraction, by combining digital signal processing and machine learning methods. We extend the scope of melody extraction research by working with a varied dataset and multiple definitions of melody. We first present an overview of the state of the art, and perform an evaluation focused on a novel symphonic music dataset. We then propose melody extraction methods based on a source-filter model and pitch contour characterisation and evaluate them on a wide range of music genres. Finally, we explore novel timbre, tonal and spatial features for contour characterisation, and propose a method for estimating multiple melodic lines. The combination of supervised and unsupervised approaches leads to advancements on melody extraction and shows a promising path for future research and applications

Trennung und Schätzung der Anzahl von Audiosignalquellen mit Zeit- und Frequenzüberlappung

Everyday audio recordings involve mixture signals: music contains a mixture of instruments; in a meeting or conference, there is a mixture of human voices. For these mixtures, automatically separating or estimating the number of sources is a challenging task. A common assumption when processing mixtures in the time-frequency domain is that sources are not fully overlapped. However, in this work we consider some cases where the overlap is severe — for instance, when instruments play the same note (unison) or when many people speak concurrently ("cocktail party") — highlighting the need for new representations and more powerful models. To address the problems of source separation and count estimation, we use conventional signal processing techniques as well as deep neural networks (DNN). We first address the source separation problem for unison instrument mixtures, studying the distinct spectro-temporal modulations caused by vibrato. To exploit these modulations, we developed a method based on time warping, informed by an estimate of the fundamental frequency. For cases where such estimates are not available, we present an unsupervised model, inspired by the way humans group time-varying sources (common fate). This contribution comes with a novel representation that improves separation for overlapped and modulated sources on unison mixtures but also improves vocal and accompaniment separation when used as an input for a DNN model. Then, we focus on estimating the number of sources in a mixture, which is important for real-world scenarios. Our work on count estimation was motivated by a study on how humans can address this task, which lead us to conduct listening experiments, confirming that humans are only able to estimate the number of up to four sources correctly. To answer the question of whether machines can perform similarly, we present a DNN architecture, trained to estimate the number of concurrent speakers. Our results show improvements compared to other methods, and the model even outperformed humans on the same task. In both the source separation and source count estimation tasks, the key contribution of this thesis is the concept of “modulation”, which is important to computationally mimic human performance. Our proposed Common Fate Transform is an adequate representation to disentangle overlapping signals for separation, and an inspection of our DNN count estimation model revealed that it proceeds to find modulation-like intermediate features.Im Alltag sind wir von gemischten Signalen umgeben: Musik besteht aus einer Mischung von Instrumenten; in einem Meeting oder auf einer Konferenz sind wir einer Mischung menschlicher Stimmen ausgesetzt. Für diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine häufige Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollständig überlappen. In dieser Arbeit betrachten wir jedoch einige Fälle, in denen die Überlappung immens ist zum Beispiel, wenn Instrumente den gleichen Ton spielen (unisono) oder wenn viele Menschen gleichzeitig sprechen (Cocktailparty) —, so dass neue Signal-Repräsentationen und leistungsfähigere Modelle notwendig sind. Um die zwei genannten Probleme zu bewältigen, verwenden wir sowohl konventionelle Signalverbeitungsmethoden als auch tiefgehende neuronale Netze (DNN). Wir gehen zunächst auf das Problem der Quellentrennung für Unisono-Instrumentenmischungen ein und untersuchen die speziellen, durch Vibrato ausgelösten, zeitlich-spektralen Modulationen. Um diese Modulationen auszunutzen entwickelten wir eine Methode, die auf Zeitverzerrung basiert und eine Schätzung der Grundfrequenz als zusätzliche Information nutzt. Für Fälle, in denen diese Schätzungen nicht verfügbar sind, stellen wir ein unüberwachtes Modell vor, das inspiriert ist von der Art und Weise, wie Menschen zeitveränderliche Quellen gruppieren (Common Fate). Dieser Beitrag enthält eine neuartige Repräsentation, die die Separierbarkeit für überlappte und modulierte Quellen in Unisono-Mischungen erhöht, aber auch die Trennung in Gesang und Begleitung verbessert, wenn sie in einem DNN-Modell verwendet wird. Im Weiteren beschäftigen wir uns mit der Schätzung der Anzahl von Quellen in einer Mischung, was für reale Szenarien wichtig ist. Unsere Arbeit an der Schätzung der Anzahl war motiviert durch eine Studie, die zeigt, wie wir Menschen diese Aufgabe angehen. Dies hat uns dazu veranlasst, eigene Hörexperimente durchzuführen, die bestätigten, dass Menschen nur in der Lage sind, die Anzahl von bis zu vier Quellen korrekt abzuschätzen. Um nun die Frage zu beantworten, ob Maschinen dies ähnlich gut können, stellen wir eine DNN-Architektur vor, die erlernt hat, die Anzahl der gleichzeitig sprechenden Sprecher zu ermitteln. Die Ergebnisse zeigen Verbesserungen im Vergleich zu anderen Methoden, aber vor allem auch im Vergleich zu menschlichen Hörern. Sowohl bei der Quellentrennung als auch bei der Schätzung der Anzahl an Quellen ist ein Kernbeitrag dieser Arbeit das Konzept der “Modulation”, welches wichtig ist, um die Strategien von Menschen mittels Computern nachzuahmen. Unsere vorgeschlagene Common Fate Transformation ist eine adäquate Darstellung, um die Überlappung von Signalen für die Trennung zugänglich zu machen und eine Inspektion unseres DNN-Zählmodells ergab schließlich, dass sich auch hier modulationsähnliche Merkmale finden lassen

Musical source separation with deep learning and large-scale datasets

Throughout this thesis we will explore automatic music source separation by utilizing modern (at the time of writing) techniques and tools from machine learning and big data processing. The bulk of this work was carried out between 2016 and 2019. In Chapter 2 we conduct a review of source separation literature. We start by outlining a subset of applications of source separation in some depth. We describe some of the early, pioneering work in automatic source separation: Auditory Scene Analysis, and its digital counterpart, Computational Auditory Scene Analysis. We then introduce matrix decomposition-based methods such as Independent Component Analysis and Non-Negative Matrix factorization, and pitch informed methods where the separation algorithm is guided by pitch information that is known a priori. We brie y discuss user-guided methods, before conducting a thorough review of Deep Learning based source separation, including recurrent, convolutional, deep clustering-based, and Generative Adversarial Networks. We then proceed to describe common evaluation metrics and training datasets. Finally, we list a number of current challenges and drawbacks of current systems. Chapter 3 focuses on datasets for musical source separation. First we show the growth of dataset sizes for both machine learning in general and music information retrieval specifically. We give several examples of the complexities and idiosyncrasies that are intrinsic to music datasets. We then proceed to present a method for extracting ground truth data for source separation from large unstructured musical catalogs. In Chapter 4 we design a novel deep learning-based source separation algorithm. Motivation is provided by means of a musicological study1 that showed the high importance of vocals relative to other musical factors, in the minds of listeners. At the core of the vocal separation algorithm is the U-Net, a deep learning architecture that uses skip connections to preserve fine-grained detail. It was originally developed in the biomedical imaging domain, and later adapted to image-to-image translation. We adapt it to the source separation domain by treating spectrograms as images, and we use the dataset mining methods from Chapter 3 to generate sufficiently large training data. We evaluate our model objectively using standard evaluation metrics, subjectively using \crowdsourced" human subjects. To the best of our knowledge, this is the first use of U-Nets for source separation. In the introduction above we proposed joint learning to optimize source separation and other objectives. In Chapter 5 we investigate one such instance: multi-task learning of vocal removal and vocal pitch tracking. We combine the vocal separation model from Chapter 4 with a state of the art pitch salience estimation model2, exploring several ways of combining the two models. We find that vocal pitch estimation benefits from joint learning when the two tasks are trained in sequence, with the source separation model preceding the pitch estimation model. We also report benefits from fine-tuning by iteratively applying the model. Chapter 6 extends the U-Net model to multiple instruments. In order to minimize the phase artifacts that were a common issue in Chapter 4, we modify the model to operate in the complex domain. We run experiments with several loss functions: Time-domain loss, magnitude-only frequency domain loss, and joint time and frequency-domain loss. Our experiments are evaluated both objectively and subjectively, and we carry out extensive qualitative analysis to investigate the effects of complex masking. Finally, we conclude the thesis in Chapter 7 by summarizing this work and highlighting several future directions of research

Singing information processing: techniques and applications

Por otro lado, se presenta un método para el cambio realista de intensidad de voz cantada. Esta transformación se basa en un modelo paramétrico de la envolvente espectral, y mejora sustancialmente la percepción de realismo al compararlo con software comerciales como Melodyne o Vocaloid. El inconveniente del enfoque propuesto es que requiere intervención manual, pero los resultados conseguidos arrojan importantes conclusiones hacia la modificación automática de intensidad con resultados realistas. Por último, se propone un método para la corrección de disonancias en acordes aislados. Se basa en un análisis de múltiples F0, y un desplazamiento de la frecuencia de su componente sinusoidal. La evaluación la ha realizado un grupo de músicos entrenados, y muestra un claro incremento de la consonancia percibida después de la transformación propuesta.La voz cantada es una componente esencial de la música en todas las culturas del mundo, ya que se trata de una forma increíblemente natural de expresión musical. En consecuencia, el procesado automático de voz cantada tiene un gran impacto desde la perspectiva de la industria, la cultura y la ciencia. En este contexto, esta Tesis contribuye con un conjunto variado de técnicas y aplicaciones relacionadas con el procesado de voz cantada, así como con un repaso del estado del arte asociado en cada caso. En primer lugar, se han comparado varios de los mejores estimadores de tono conocidos para el caso de uso de recuperación por tarareo. Los resultados demuestran que \cite{Boersma1993} (con un ajuste no obvio de parámetros) y \cite{Mauch2014}, tienen un muy buen comportamiento en dicho caso de uso dada la suavidad de los contornos de tono extraídos. Además, se propone un novedoso sistema de transcripción de voz cantada basada en un proceso de histéresis definido en tiempo y frecuencia, así como una herramienta para evaluación de voz cantada en Matlab. El interés del método propuesto es que consigue tasas de error cercanas al estado del arte con un método muy sencillo. La herramienta de evaluación propuesta, por otro lado, es un recurso útil para definir mejor el problema, y para evaluar mejor las soluciones propuestas por futuros investigadores. En esta Tesis también se presenta un método para evaluación automática de la interpretación vocal. Usa alineamiento temporal dinámico para alinear la interpretación del usuario con una referencia, proporcionando de esta forma una puntuación de precisión de afinación y de ritmo. La evaluación del sistema muestra una alta correlación entre las puntuaciones dadas por el sistema, y las puntuaciones anotadas por un grupo de músicos expertos

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

Kompozicionalni hierarhični model za pridobivanje informacij iz glasbe

In recent years, deep architectures, most commonly based on neural networks, have advanced the state of the art in many research areas. Due to the popularity and the success of deep neural-networks, other deep architectures, including compositional models, have been put aside from mainstream research. This dissertation presents the compositional hierarchical model as a novel deep architecture for music processing. Our main motivation was to develop and explore an alternative non-neural deep architecture for music processing which would be transparent, meaning that the encoded knowledge would be interpretable, trained in an unsupervised manner and on small datasets, and useful as a feature extractor for classification tasks, as well as a transparent model for unsupervised pattern discovery. We base our work on compositional models, as compositionality is inherent in music. The proposed compositional hierarchical model learns a multi-layer hierarchical representation of the analyzed music signals in an unsupervised manner. It provides transparent insights into the learned concepts and their structure. It can be used as a feature extractor---its output can be used for classification tasks using existing machine learning techniques. Moreover, the model\u27s transparency enables an interpretation of the learned concepts, so the model can be used for analysis (exploration of the learned hierarchy) or discovery-oriented (inferring the hierarchy) tasks, which is difficult with most neural network based architectures. The proposed model uses relative coding of the learned concepts, which eliminates the need for large annotated training datasets that are essential in deep architectures with a large number of parameters. Relative coding contributes to slim models, which are fast to execute and have low memory requirements. The model also incorporates several biologically-inspired mechanisms that are modeled according to the mechanisms that exists at the lower levels of human perception (eg~ lateral inhibition in the human ear) and that significantly affect perception. The proposed model is evaluated on several music information retrieval tasks and its results are compared to the current state of the art. The dissertation is structured as follows. In the first chapter we present the motivation for the development of the new model. In the second chapter we elaborate on the related work in music information retrieval and review other compositional and transparent models. Chapter three introduces a thorough description of the proposed model. The model structure, its learning and inference methods are explained, as well as the incorporated biologically-inspired mechanisms. The model is then applied to several different music domains, which are divided according to the type of input data. In this we follow the timeline of the development and the implementation of the model. In chapter four, we present the model\u27s application to audio recordings, specifically for two tasks: automatic chord estimation and multiple fundamental frequency estimation. In chapter five, we present the model\u27s application to symbolic music representations. We concentrate on pattern discovery, emphasizing the model\u27s ability to tackle such problems. We also evaluate the model as a feature generator for tune family classification. Finally, in chapter six, we show the latest progress in developing the model for representing rhythm and show that it exhibits a high degree of robustness in extracting high-level rhythmic structures from music signals. We conclude the dissertation by summarizing our work and the results, elaborating on forthcoming work in the development of the model and its future applications.S porastom globokih arhitektur, ki temeljijo na nevronskih mrežah, so se v zadnjem času bistveno izboljšali rezultati pri reševanju problemov na več področjih. Zaradi popularnosti in uspešnosti teh globokih pristopov, temelječih na nevronskih mrežah, so bili drugi, predvsem kompozicionalni pristopi, odmaknjeni od središča pozornosti raziskav. V pričujoči disertaciji se posvečamo vprašanju, ali je mogoče razviti globoko arhitekturo, ki bo presegla obstoječe probleme globokih arhitektur. S tem namenom se vračamo h kompozicionalnim modelom in predstavimo kompozicionalni hierarhični model kot alternativno globoko arhitekturo, ki bo imela naslednje značilnosti: transparentnost, ki omogoča enostavno razlago naučenih konceptov, nenadzorovano učenje in zmožnost učenja na majhnih podatkovnih bazah, uporabnost modela kot izluščevalca značilk, kot tudi zmožnost uporabe transparentnosti modela za odkrivanje vzorcev. Naše delo temelji na kompozicionalnih modelih, ki so v glasbi intuitivni. Predlagani kompozicionalni hierarhični model je zmožen nenadzorovanega učenja večnivojske predstavitve glasbenega vhoda. Model omogoča pregled naučenih konceptov skozi transparentne strukture. Lahko ga uporabimo kot generator značilk -- izhod modela lahko uporabimo za klasifikacijo z drugimi pristopi strojnega učenja. Hkrati pa lahko transparentnost predlaganega modela uporabimo za analizo (raziskovanje naučene hierarhije) pri odkrivanju vzorcev, kar je težko izvedljivo z ostalimi pristopi, ki temeljijo na nevronskih mrežah. Relativno kodiranje konceptov v samem modelu pripomore k precej manjšim modelom in posledično zmanjšuje potrebo po velikih podatkovnih zbirkah, potrebnih za učenje modela. Z vpeljavo biološko navdahnjenih mehanizmov želimo model še bolj približati človeškemu načinu zaznave. Za nekatere mehanizme, na primer inhibicijo, vemo, da so v človeški percepciji prisotni na nižjih nivojih v ušesu in bistveno vplivajo na način zaznave. V modelu uvedemo prve korake k takšnemu načinu procesiranja proti končnemu cilju izdelave modela, ki popolnoma odraža človeško percepcijo. V prvem poglavju disertacije predstavimo motivacijo za razvoj novega modela. V drugem poglavju se posvetimo dosedanjim objavljenim dosežkom na tem področju. V nadaljnjih poglavjih se osredotočimo na sam model. Sprva opišemo teoretično zasnovo modela in način učenja ter delovanje biološko-navdahnjenih mehanizmov. V naslednjem koraku model apliciramo na več različnih glasbenih domen, ki so razdeljene glede na tip vhodnih podatkov. Pri tem sledimo časovnici razvoja in implementacijam modela tekom doktorskega študija. Najprej predstavimo aplikacijo modela za časovno-frekvenčne signale, na katerem model preizkusimo za dve opravili: avtomatsko ocenjevanje harmonij in avtomatsko transkripcijo osnovnih frekvenc. V petem poglavju predstavimo drug način aplikacije modela, tokrat na simbolne vhodne podatke, ki predstavljajo glasbeni zapis. Pri tem pristopu se osredotočamo na odkrivanje vzorcev, s čimer poudarimo zmožnost modela za reševanje tovrstnih problemov, ki je ostalim pristopom še nedosegljivo. Model prav tako evalviramo v vlogi generatorja značilk. Pri tem ga evalviramo na problemu melodične podobnosti pesmi in razvrščanja v variantne tipe. Nazadnje, v šestem poglavju, pokažemo zadnji dosežek razvoja modela, ki ga apliciramo na problem razumevanja ritma v glasbi. Prilagojeni model analiziramo in pokažemo njegovo zmožnost učenja različnih ritmičnih oblik in visoko stopnjo robustnosti pri izluščevanju visokonivojskih struktur v ritmu. V zaključkih disertacije povzamemo vloženo delo in rezultate ter nakažemo nadaljnje korake za razvoj modela v prihodnosti

Articulatory Copy Synthesis Based on the Speech Synthesizer VocalTractLab

Articulatory copy synthesis (ACS), a subarea of speech inversion, refers to the reproduction of natural utterances and involves both the physiological articulatory processes and their corresponding acoustic results. This thesis proposes two novel methods for the ACS of human speech using the articulatory speech synthesizer VocalTractLab (VTL) to address or mitigate the existing problems of speech inversion, such as non-unique mapping, acoustic variation among different speakers, and the time-consuming nature of the process. The first method involved finding appropriate VTL gestural scores for given natural utterances using a genetic algorithm. It consisted of two steps: gestural score initialization and optimization. In the first step, gestural scores were initialized using the given acoustic signals with speech recognition, grapheme-to-phoneme (G2P), and a VTL rule-based method for converting phoneme sequences to gestural scores. In the second step, the initial gestural scores were optimized by a genetic algorithm via an analysis-by-synthesis (ABS) procedure that sought to minimize the cosine distance between the acoustic features of the synthetic and natural utterances. The articulatory parameters were also regularized during the optimization process to restrict them to reasonable values. The second method was based on long short-term memory (LSTM) and convolutional neural networks, which were responsible for capturing the temporal dependence and the spatial structure of the acoustic features, respectively. The neural network regression models were trained, which used acoustic features as inputs and produced articulatory trajectories as outputs. In addition, to cover as much of the articulatory and acoustic space as possible, the training samples were augmented by manipulating the phonation type, speaking effort, and the vocal tract length of the synthetic utterances. Furthermore, two regularization methods were proposed: one based on the smoothness loss of articulatory trajectories and another based on the acoustic loss between original and predicted acoustic features. The best-performing genetic algorithms and convolutional LSTM systems (evaluated in terms of the difference between the estimated and reference VTL articulatory parameters) obtained average correlation coefficients of 0.985 and 0.983 for speaker-dependent utterances, respectively, and their reproduced speech achieved recognition accuracies of 86.25% and 64.69% for speaker-independent utterances of German words, respectively. When applied to German sentence utterances, as well as English and Mandarin Chinese word utterances, the neural network based ACS systems achieved recognition accuracies of 73.88%, 52.92%, and 52.41%, respectively. The results showed that both of these methods not only reproduced the articulatory processes but also reproduced the acoustic signals of reference utterances. Moreover, the regularization methods led to more physiologically plausible articulatory processes and made the estimated articulatory trajectories be more articulatorily preferred by VTL, thus reproducing more natural and intelligible speech. This study also found that the convolutional layers, when used in conjunction with batch normalization layers, automatically learned more distinctive features from log power spectrograms. Furthermore, the neural network based ACS systems trained using German data could be generalized to the utterances of other languages

Compositional hierarchical model for music information retrieval

In recent years, deep architectures, most commonly based on neural networks, have advanced the state of the art in many research areas. Due to the popularity and the success of deep neural-networks, other deep architectures, including compositional models, have been put aside from mainstream research. This dissertation presents the compositional hierarchical model as a novel deep architecture for music processing. Our main motivation was to develop and explore an alternative non-neural deep architecture for music processing which would be transparent, meaning that the encoded knowledge would be interpretable, trained in an unsupervised manner and on small datasets, and useful as a feature extractor for classification tasks, as well as a transparent model for unsupervised pattern discovery. We base our work on compositional models, as compositionality is inherent in music. The proposed compositional hierarchical model learns a multi-layer hierarchical representation of the analyzed music signals in an unsupervised manner. It provides transparent insights into the learned concepts and their structure. It can be used as a feature extractor—its output can be used for classification tasks using existing machine learning techniques. Moreover, the model’s transparency enables an interpretation of the learned concepts, so the model can be used for analysis (exploration of the learned hierarchy) or discovery-oriented (inferring the hierarchy) tasks, which is difficult with most neural network based architectures. The proposed model uses relative coding of the learned concepts, which eliminates the need for large annotated training datasets that are essential in deep architectures with a large number of parameters. Relative coding contributes to slim models, which are fast to execute and have low memory requirements. The model also incorporates several biologically-inspired mechanisms that are modeled according to the mechanisms that exists at the lower levels of human perception (e.g. lateral inhibition in the human ear) and that significantly affect perception. The proposed model is evaluated on several music information retrieval tasks and its results are compared to the current state of the art. The dissertation is structured as follows. In the first chapter we present the motivation for the development of the new model. In the second chapter we elaborate on the related work in music information retrieval and review other compositional and transparent models. Chapter three introduces a thorough description of the proposed model. The model structure, its learning and inference methods are explained, as well as the incorporated biologically-inspired mechanisms. The model is then applied to several different music domains, which are divided according to the type of input data. In this we follow the timeline of the development and the implementation of the model. In chapter four, we present the model’s application to audio recordings, specifically for two tasks: automatic chord estimation and multiple fundamental frequency estimation. In chapter five, we present the model’s application to symbolic music representations. We concentrate on pattern discovery, emphasizing the model’s ability to tackle such problems. We also evaluate the model as a feature generator for tune family classification. Finally, in chapter six, we show the latest progress in developing the model for representing rhythm and show that it exhibits a high degree of robustness in extracting high-level rhythmic structures from music signals. We conclude the dissertation by summarizing our work and the results, elaborating on forthcoming work in the development of the model and its future applications

Compositional hierarchical model for music information retrieval

In recent years, deep architectures, most commonly based on neural networks, have advanced the state of the art in many research areas. Due to the popularity and the success of deep neural-networks, other deep architectures, including compositional models, have been put aside from mainstream research. This dissertation presents the compositional hierarchical model as a novel deep architecture for music processing. Our main motivation was to develop and explore an alternative non-neural deep architecture for music processing which would be transparent, meaning that the encoded knowledge would be interpretable, trained in an unsupervised manner and on small datasets, and useful as a feature extractor for classification tasks, as well as a transparent model for unsupervised pattern discovery. We base our work on compositional models, as compositionality is inherent in music. The proposed compositional hierarchical model learns a multi-layer hierarchical representation of the analyzed music signals in an unsupervised manner. It provides transparent insights into the learned concepts and their structure. It can be used as a feature extractor—its output can be used for classification tasks using existing machine learning techniques. Moreover, the model’s transparency enables an interpretation of the learned concepts, so the model can be used for analysis (exploration of the learned hierarchy) or discovery-oriented (inferring the hierarchy) tasks, which is difficult with most neural network based architectures. The proposed model uses relative coding of the learned concepts, which eliminates the need for large annotated training datasets that are essential in deep architectures with a large number of parameters. Relative coding contributes to slim models, which are fast to execute and have low memory requirements. The model also incorporates several biologically-inspired mechanisms that are modeled according to the mechanisms that exists at the lower levels of human perception (e.g. lateral inhibition in the human ear) and that significantly affect perception. The proposed model is evaluated on several music information retrieval tasks and its results are compared to the current state of the art. The dissertation is structured as follows. In the first chapter we present the motivation for the development of the new model. In the second chapter we elaborate on the related work in music information retrieval and review other compositional and transparent models. Chapter three introduces a thorough description of the proposed model. The model structure, its learning and inference methods are explained, as well as the incorporated biologically-inspired mechanisms. The model is then applied to several different music domains, which are divided according to the type of input data. In this we follow the timeline of the development and the implementation of the model. In chapter four, we present the model’s application to audio recordings, specifically for two tasks: automatic chord estimation and multiple fundamental frequency estimation. In chapter five, we present the model’s application to symbolic music representations. We concentrate on pattern discovery, emphasizing the model’s ability to tackle such problems. We also evaluate the model as a feature generator for tune family classification. Finally, in chapter six, we show the latest progress in developing the model for representing rhythm and show that it exhibits a high degree of robustness in extracting high-level rhythmic structures from music signals. We conclude the dissertation by summarizing our work and the results, elaborating on forthcoming work in the development of the model and its future applications