Lorenzo J. Tardón

Automatic Transcription of Guitar Chords and Fingering From Audio

This paper proposes a method for extracting the fingering configurations automatically from a recorded guitar performance. 330 different fingering configurations are considered, corresponding to different versions of the major, minor, major 7th, and minor 7th chords played on the guitar fretboard. The method is formulated as a hidden Markov model, where the hidden states correspond to the different fingering configurations and the observed acoustic features are obtained from a multiple fundamental frequency estimator that measures the salience of a range of candidate note pitches within individual time frames. Transitions between consecutive fingerings are constrained by a musical model trained on a database of chord sequences, and a heuristic cost function that measures the physical difficulty of moving from one configuration of finger positions to another. The method was evaluated on recordings from the acoustic, electric, and the Spanish guitar and clearly outperformed a non-guitar-specific reference chord transcription method despite the fact that the number of chords considered here is significantly larger.

Information Dissemination in VANETs based upon a Tree Topology

Abstract In this work, we focus on vehicular ad hoc networks, also called VANETs, which are communication networks that devices (in this case vehicles) use to exchange messages in a decentralized fashion, i.e., using no preexisting infrastructure. We first assess the feasibility of relying on a tree-based topology management structure for mobile ad hoc networks and in particular for VANETs. Next, we enhance DAGRS, an existing decentralized model for enabling distributed tree management and build BODYF on it, an efficient broadcast algorithm. Several broadcasting algorithms of the state of the art are implemented in order to compare the performance of BODYF. The approach is validated by simulation through three realistic scenarios located in Luxembourg city: the city center for both pedestrian and vehicles, and a highway environment. The comparison is made in terms of the coverage achieved by the broadcasting process as well as the complexity of the messages. DAGRS/BODYF approach outperforms other existing protocols in terms of both the number of devices reached and the network use.

Optical music recognition for scores written in white mensural notation

An Optical Music Recognition (OMR) system especially adapted for handwritten musical scores of the XVII-th and the early XVIII-th centuries written in white mensural notation is presented. The system performs a complete sequence of analysis stages: the input is the RGB image of the score to be analyzed and, after a preprocessing that returns a black and white image with corrected rotation, the staves are processed to return a score without staff lines; then, a music symbol processing stage isolates the music symbols contained in the score and, finally, the classification process starts to obtain the transcription in a suitable electronic format so that it can be stored or played. This work will help to preserve our cultural heritage keeping the musical information of the scores in a digital format that also gives the possibility to perform and distribute the original music contained in those scores.

Inharmonicity-Based Method for the Automatic Generation of Guitar Tablature

In this paper, a system for the extraction of the tablature of guitar musical pieces using only the audio waveform is presented. The analysis of the inharmonicity relations between the fundamentals and the partials of the notes played is the main process that allows to estimate both the notes played and the string/fret combination that was used to produce that sound. A procedure to analyze chords will also be described. This procedure will also make use of the inharmonicity analysis to find the simultaneous string/fret combinations used to play each chord. The proposed method is suitable for any guitar type: classical, acoustic and electric guitars. The system performance has been evaluated on a series of guitar samples from the RWC instruments database and our own recordings.

Transcription and expressiveness detection system for violin music

In this paper, a transcription system for music played by violin is presented. The transcription system not only detects the pitch and duration of the notes but also identifies successfully the employed technique to play each note: détaché with and without accent and with and without vibrato, pizzicato, tremolo, spiccato and flageolett-töne. The transcription system is based on a combined analysis of the time domain and frequency domain properties of the music signal.

SiPTH: singing transcription based on hysteresis defined on the pitch-time curve

In this paper, we present a method for monophonic singing transcription based on hysteresis defined on the pitch-time curve. This method is designed to perform note segmentation even when the pitch evolution during the same note behaves unstably, as in the case of untrained singers. The selected approach estimates the regions in which the chroma is stable, these regions are classified as voiced or unvoiced according to a decision tree classifier using two descriptors based on aperiodicity and power. Then, a note segmentation stage based on pitch intervals of the sung signal is carried out. To this end, a dynamic averaging of the pitch curve is performed after the beginning of a note is detected in order to roughly estimate the pitch. Deviations of the actual pitch curve with respect to this average are measured to determine the next note change according to a hysteresis process defined on the pitch-time curve. Finally, each note is labeled using three single values: rounded pitch (to semitones), duration and volume. Also, a complete evaluation methodology that includes the definition of different relevant types of errors, measures and a method for the computation of the evaluation measures are presented. The proposed system improves significantly the performance of the baseline approach, and attains results similar to previous approaches.

Fundamental frequency alignment vs. note-based melodic similarity for singing voice assessment

This paper presents a generic approach for automatic singing assessment for basic singing levels. The system provides the user with a set of intonation, rhythm and overall ratings obtained by measuring the similarity of the sung melody and a target performance. Two different similarity approaches are discussed: f0 curve alignment through Dynamic Time Warping (DTW), and singing transcription plus note-level similarity. From these two approaches, we extract different intonation and rhythm similarity measures which are combined through quadratic polynomial regression analysis in order to fit the judgement of 4 trained musicians on 27 performances. The results show that the proposed system is suitable for automatic singing voice rating and that DTW based measures are specially simple and effective for intonation and rhythm assessment.

Towards connectivity improvement in VANETs using bypass links

VANETs are ad hoc networks in which devices are vehicles moving at high speeds. This kind of network is getting more and more importance since it has many practical and important applications, like multimedia file sharing (e.g., maps, music, news, weather), or dissemination of alarm messages (e.g., accidents, traffic jams, bad road conditions). One important problem faced in ad hoc networks is network partitioning, causing the formation of isolated clusters, and preventing devices in different clusters from communicating. Usually, devices composing the ad hoc network are provided with other communication interfaces rather than Wi-Fi and/or Bluetooth that allow them to connect to remote devices, such as GPRS/HSDPA. Additionally, there exists some network infrastructure in cities or roads that could be used by VANETs (e.g. hotspots). By taking advantage of these technologies and infrastructures, devices could be able to form a hybrid network, establishing remote links between them (called bypass links) in order to improve the network connectivity by joining, for example, separate clusters. In this work, we face the problem of optimizing the number and location of these remote connections for maximizing the QoS of the network. We use an efficient genetic algorithm with structured population, called cellular genetic algorithm (cGA), to optimize this hard problem. The evaluation of the quality of the network connectivity is made using small world properties. Our goal is to find highly accurate solutions (that could be used as reference values for future works) and then analyze the influence of the quality of the solutions in the real behavior of the network. This is achieved by using the JANE simulator to disseminate a message in the network using two broadcasting protocols having different features.

Automatic melody composition based on a probabilistic model of music style and harmonic rules

Abstract The aim of the present work is to perform a step towards the design of specific algorithms and methods for automatic music generation. A novel probabilistic model for the characterization of music learned from music samples is designed. This model makes use of automatically extracted music parameters, namely tempo, time signature, rhythmic patterns and pitch contours, to characterize music. Specifically, learned rhythmic patterns and pitch contours are employed to characterize music styles. Then, a novel autonomous music compositor that generates new melodies using the model developed will be presented. The methods proposed in this paper take into consideration different aspects related to the traditional way in which music is composed by humans such as harmony evolution and structure repetitions and apply them together with the probabilistic reutilization of rhythm patterns and pitch contours learned beforehand to compose music pieces.

A novel Markovian formulation of the correspondence problem in stereo vision

This paper addresses the problem of finding matching points in stereo image pairs, i.e., the problem of correspondence. Even though this topic is well-known, a complete probabilistic formulation of it using psychovisual cues is still missing. We propose a novel Bayesian model based on Markov Random Fields (MRFs); the prior energy function is built in terms of the probability density function (pdf) of the disparity gradient. This pdf has never been reported in the past. The likelihood energy function is defined in terms of the pdf of the square normalized cross covariance between any two matching points. The stereo correspondence map is then obtained as the MAP estimator of the posterior field. Comparative results with methods previously reported, show the adequacy of the general model here proposed, and a good compromise between deterministic and stochastic images is attained.