Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Music fingerprinting based on bhattacharya distance for song and cover song recognition

People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science

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