Timbre-invariant Audio Features for Style Analysis of Classical Music

Copyright: (c) 2014 Christof Weiß et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Signal Processing Methods for Music Synchronization, Audio Matching, and Source Separation

The field of music information retrieval (MIR) aims at developing techniques and tools for organizing, understanding, and searching multimodal information in large music collections in a robust, efficient and intelligent manner. In this context, this thesis presents novel, content-based methods for music synchronization, audio matching, and source separation. In general, music synchronization denotes a procedure which, for a given position in one representation of a piece of music, determines the corresponding position within another representation. Here, the thesis presents three complementary synchronization approaches, which improve upon previous methods in terms of robustness, reliability, and accuracy. The first approach employs a late-fusion strategy based on multiple, conceptually different alignment techniques to identify those music passages that allow for reliable alignment results. The second approach is based on the idea of employing musical structure analysis methods in the context of synchronization to derive reliable synchronization results even in the presence of structural differences between the versions to be aligned. Finally, the third approach employs several complementary strategies for increasing the accuracy and time resolution of synchronization results. Given a short query audio clip, the goal of audio matching is to automatically retrieve all musically similar excerpts in different versions and arrangements of the same underlying piece of music. In this context, chroma-based audio features are a well-established tool as they possess a high degree of invariance to variations in timbre. This thesis describes a novel procedure for making chroma features even more robust to changes in timbre while keeping their discriminative power. Here, the idea is to identify and discard timbre-related information using techniques inspired by the well-known MFCC features, which are usually employed in speech processing. Given a monaural music recording, the goal of source separation is to extract musically meaningful sound sources corresponding, for example, to a melody, an instrument, or a drum track from the recording. To facilitate this complex task, one can exploit additional information provided by a musical score. Based on this idea, this thesis presents two novel, conceptually different approaches to source separation. Using score information provided by a given MIDI file, the first approach employs a parametric model to describe a given audio recording of a piece of music. The resulting model is then used to extract sound sources as specified by the score. As a computationally less demanding and easier to implement alternative, the second approach employs the additional score information to guide a decomposition based on non-negative matrix factorization (NMF)

Automated methods for audio-based music analysis with applications to musicology

This thesis contributes to bridging the gap between music information retrieval (MIR) and musicology. We present several automated methods for music analysis, which are motivated by concrete application scenarios being of central importance in musicology. In this context, the automated music analysis is performed on the basis of audio material. Here, one reason is that for a given piece of music usually many different recorded performances exist. The availability of multiple versions of a piece of music is exploited in this thesis to stabilize analysis results. We show how the presented automated methods open up new possibilities for supporting musicologists in their work. Furthermore, we introduce novel interdisciplinary concepts which facilitate the collaboration between computer scientists and musicologists. Based on these concepts, we demonstrate how MIR researchers and musicologists may greatly benefit from each other in an interdisciplinary collaboration. Firstly, we present a fully automatic approach for the extraction of tempo parameters from audio recordings and show to which extent this approach may support musicologists in analyzing recorded performances. Secondly, we introduce novel user interfaces which are aimed at encouraging the exchange between computer science and musicology. In this context, we indicate the potential of computer-based methods in music education by testing and evaluating a novel MIR user interface at the University of Music Saarbrücken. Furthermore, we show how a novel multi-perspective user interface allows for interactively viewing and evaluating version-dependent analysis results and opens up new possibilities for interdisciplinary collaborations. Thirdly, we present a cross-version approach for harmonic analysis of audio recordings and demonstrate how this approach enables musicologists to explore harmonic structures even across large music corpora. Here, one simple yet important conceptual contribution is to convert the physical time axis of an audio recording into a performance-independent musical time axis given in bars.Diese Arbeit trägt dazu bei, die Brücke zwischen der automatisierten Musikverarbeitung und der Musikwissenschaft zu schlagen. Ausgehend von Anwendungen, die in der Musikwissenschaft von zentraler Bedeutung sind, stellen wir verschiedene automatisierte Verfahren vor. Die automatisierte Musikanalyse wird hierbei auf der Basis von Audiodaten durchgeführt. Ein Grund hierfür ist, dass zu einem gegebenen Musikstück üblicherweise viele verschiedene Aufnahmen existieren. Die Verfügbarkeit mehrerer Versionen zu ein und demselben Musikstück wird in dieser Arbeit ausgenutzt, um Analyseresultate zu stabilisieren. Wir demonstrieren, inwieweit die vorgestellten automatisierten Methoden neue Möglichkeiten eröffnen, Musikwissenschaftler in ihrer Arbeit zu unterstützen. Außerdem führen wir neue interdisziplinäre Konzepte ein, die die Kollaboration zwischen Informatikern und Musikwissenschaftlern erleichtern. Auf der Basis dieser Konzepte zeigen wir, dass Informatiker und Musikwissenschaftler im Rahmen einer interdisziplinären Kollaboration erheblich voneinander profitieren können. Erstens stellen wir ein vollautomatisches Verfahren zur Extraktion von Tempoparametern aus Audioaufnahmen vor und zeigen, inwieweit dieses Verfahren Musikwissenschaftler bei der Interpretationsanalyse verschiedener Aufnahmen unterstützen kann. Zweitens führen wir neuartige Benutzerschnittstellen ein, die darauf abzielen, den Austausch zwischen der Informatik und der Musikwissenschaft zu fördern. In diesem Zusammenhang testen und evaluieren wir eine Benutzerschnittstelle an der Hochschule für Musik Saar und deuten auf diese Weise das Potential computer-basierter Methoden im Bereich der Musikerziehung an. Weiterhin stellen wir eine neuartige Benutzerschnittstelle vor, die es auf interaktive Weise ermöglicht, verschiedene Sichtweisen auf versionsabhängige Analyseresultate einzunehmen und diese auszuwerten. Diese Benutzerschnittstelle eröffnet neue Möglichkeiten für interdisziplinäre Kollaborationen. Drittens zeigen wir, wie eine cross-version harmonische Analyse es Musikwissenschaftlern ermöglicht, harmonische Strukturen über riesige musikalische Werkzyklen hinweg zu ergründen. In diesem Zusammenhang ist ein einfacher aber wichtiger konzeptueller Beitrag, die physikalische Zeitachse einer Audioaufnahme in eine versionsunabhängige musikalische Zeitachse gegeben in Takten zu verwandeln

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

Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines

The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years