Deep Polyphonic ADSR Piano Note Transcription

We investigate a late-fusion approach to piano transcription, combined with a strong temporal prior in the form of a handcrafted Hidden Markov Model (HMM). The network architecture under consideration is compact in terms of its number of parameters and easy to train with gradient descent. The network outputs are fused over time in the final stage to obtain note segmentations, with an HMM whose transition probabilities are chosen based on a model of attack, decay, sustain, release (ADSR) envelopes, commonly used for sound synthesis. The note segments are then subject to a final binary decision rule to reject too weak note segment hypotheses. We obtain state-of-the-art results on the MAPS dataset, and are able to outperform other approaches by a large margin, when predicting complete note regions from onsets to offsets.Comment: 5 pages, 2 figures, published as ICASSP'1

Template Adaptation for Improving Automatic Music Transcription

In this work, we propose a system for automatic music transcription which adapts dictionary templates so that they closely match the spectral shape of the instrument sources present in each recording. Current dictionary-based automatic transcription systems keep the input dictionary fixed, thus the spectral shape of the dictionary components might not match the shape of the test instrument sources. By performing a conservative transcription pre-processing step, the spectral shape of detected notes can be extracted and utilized in order to adapt the template dictionary. We propose two variants for adaptive transcription, namely for single-instrument transcription and for multiple-instrument transcription. Experiments are carried out using the MAPS and Bach10 databases. Results in terms of multi-pitch detection and instrument assignment show that there is a clear and consistent improvement when adapting the dictionary in contrast with keeping the dictionary fixed

Template Adaptation for Improving Automatic Music Transcription

publicationstatus: publishedpublicationstatus: publishedpublicationstatus: publishedIn this work, we propose a system for automatic music transcription which adapts dictionary templates so that they closely match the spectral shape of the instrument sources present in each recording. Current dictionary-based automatic transcription systems keep the input dictionary fixed, thus the spectral shape of the dictionary components might not match the shape of the test instrument sources. By performing a conservative transcription pre-processing step, the spectral shape of detected notes can be extracted and utilized in order to adapt the template dictionary. We propose two variants for adaptive transcription, namely for single-instrument transcription and for multiple-instrument transcription. Experiments are carried out using the MAPS and Bach10 databases. Results in terms of multi-pitch detection and instrument assignment show that there is a clear and consistent improvement when adapting the dictionary in contrast with keeping the dictionary fixed

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

Contributions to speech processing and ambient sound analysis

We are constantly surrounded by sounds that we continuously exploit to adapt our actions to situations we are facing. Some of the sounds like speech can have a particular structure from which we can infer some information, explicit or not. This is one reason why speech is possibly that is the most intuitive way to communicate between humans. Within the last decade, there has been significant progress in the domain of speech andaudio processing and in particular in the domain of machine learning applied to speech and audio processing. Thanks to these progresses, speech has become a central element in many human to human distant communication tools as well as in human to machine communication systems. These solutions work pretty well on clean speech or under controlled condition. However, in scenarios that involve the presence of acoustic perturbation such as noise or reverberation systems performance tends to degrade severely. In this thesis we focus on processing speech and its environments from an audio perspective. The algorithms proposed here are relying on a variety of solutions from signal processing based approaches to data-driven solutions based on supervised matrix factorization or deep neural networks. We propose solutions to problems ranging from speech recognition, to speech enhancement or ambient sound analysis. The target is to offer a panorama of the different aspects that could improve a speech processing algorithm working in a real environments. We start by describing automatic speech recognition as a potential end application and progressively unravel the limitations and the proposed solutions ending-up to the more general ambient sound analysis.Nous sommes constamment entourés de sons que nous exploitons pour adapter nos actions aux situations auxquelles nous sommes confrontés. Certains sons comme la parole peuvent avoir une structure particulière à partir de laquelle nous pouvons déduire des informations, explicites ou non. C’est l’une des raisons pour lesquelles la parole est peut-être le moyen le plus intuitif de communiquer entre humains. Au cours de la décennie écoulée, des progrès significatifs ont été réalisés dans le domaine du traitement de la parole et du son et en particulier dans le domaine de l’apprentissage automatique appliqué au traitement de la parole et du son. Grâce à ces progrès, la parole est devenue un élément central de nombreux outils de communication à distance d’humain à humain ainsi que dans les systèmes de communication humain-machine. Ces solutions fonctionnent bien sur un signal de parole propre ou dans des conditions contrôlées. Cependant, dans les scénarios qui impliquent la présence de perturbations acoustiques telles que du bruit ou de la réverbération les performances peuvent avoir tendance à se dégrader gravement. Dans cette HDR, nous nous concentrons sur le traitement de la parole et de son environnement d’un point de vue audio. Les algorithmes proposés ici reposent sur une variété de solutions allant des approches basées sur le traitement du signal aux solutions orientées données à base de factorisation matricielle supervisée ou de réseaux de neurones profonds. Nous proposons des solutions à des problèmes allant de la reconnaissance vocale au rehaussement de la parole ou à l’analyse des sons ambiants. L’objectif est d’offrir un panorama des différents aspects qui pourraient être améliorer un algorithme de traitement de la parole fonctionnant dans un environnement réel. Nous commençons par décrire la reconnaissance automatique de la parole comme une application finale potentielle et analysons progressivement les limites et les solutions proposées aboutissant à l’analyse plus générale des sons ambiants