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.

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.

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.