From an early age, individuals are continuously exposed to other languages beyond their native tongue; however, the brain’s response to these auditory stimuli remains unclear. To investigate this, an experiment was designed to record electroencephalography (EEG) signals from subjects listening to sentences in five different languages, and a specific database was built to enable performing classification tests to distinguish between different languages, and varying levels of language comprehension. By analysing the energy difference between the EEG channels to characterize these signals, different classification tests were conducted using bidirectional Long Short-Term Memory (bi-LSTM), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks . The main objective is the analysis of the brain’s response in two different scenarios: when the subject listens to sentences in different languages, and when the subject understands or misunderstands the meaning of a sentence. In the multi-class classification involving sentences in five different languages, the accuracy attained is 36.37 %. However, in the multi-class classification between ‘understood’/‘understood part of the meaning’/‘didn’t understand’, the accuracy attained reaches 81.36 %. The results obtained for binary classification tests of understand native language or foreign language is 89.09 %. The bi-LSTM neural network achieved the overall best performance. These results demonstrate that the analysis of the EEG signals alone can give information regarding a person’s language comprehension level, and can be used for monitoring the learning curve of a new language or to assess comprehension in patients with conditions such as aphasia .

Abstract In this paper, a system to build music in an intuitive and accessible way, with Lego bricks, is presented. The system makes use of the new powerful and cheap possibilities that technology offers for making old things in a new way. The Raspberry Pi is used to control the system and run the necessary algorithms, customized Lego bricks are used for building melodies, custom electronic designs, software pieces and 3D printed parts complete the items employed. The system designed is modular, it allows creating melodies with chords and percussion or just melodies or perform as a beatbox or a melody box. The main interaction with the system is made using Lego-type building blocks. Tests have demonstrated its versatility and ease of use, as well as its usefulness in music learning for both children and adults.

Abstract The human brain receives stimuli in multiple ways; among them, audio constitutes an important source of relevant stimuli for the brain regarding communication, amusement, warning, etc. In this context, the aim of this manuscript is to advance in the classification of brain responses to music of diverse genres and to sounds of different nature: speech and music. For this purpose, two different experiments have been designed to acquire EEG signals from subjects listening to songs of different musical genres and sentences in various languages. With this, a novel scheme is proposed to characterize brain signals for their classification; this scheme is based on the construction of a feature matrix built on relations between energy measured at the different EEG channels and the usage of a bi-LSTM neural network. With the data obtained, evaluations regarding EEG-based classification between speech and music, different musical genres, and whether the subject likes the song listened to or not are carried out. The experiments unveil satisfactory performance to the proposed scheme. The results obtained for binary audio type classification attain 98.66% of success. In multi-class classification between 4 musical genres, the accuracy attained is 61.59%, and results for binary classification of musical taste rise to 96.96%.

Automatic music transcription can be defined as the analysis of the acoustic signal to extract a symbolic representation of music. Existing transcription systems typically consider just the notes played at a given moment; however, other aspects such as expressiveness and playing technique can also be considered. This work is focused on how chords are played. Specifically, we consider a special type of chords, those played in arpeggio style, or simply arpeggios, in which the notes are played fast, sequentially from the lowest to the highest pitched note or vice versa and with a large overlap of the notes' sound. The main goal of this paper is to determine the pitch direction in which the arpeggiated chord was played. Two different classification methods are considered: a Fisher linear discriminant and an SVM linear classification scheme. Different features are presented for this task: one is based on the Mel-frequency cepstral coefficients (MFCCs) and two others, specifically designed for this task, rely on different analyses of the spectrogram. Evaluations have been done with a wide number of musical instruments. The results show that the pitch direction can be reliably detected using the proposed methods.

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.

In this paper, we propose a method to synthesize the natural variations of spectral envelope as intensity varies in singing voice. To this end, we propose a parametric model of spectral envelope based on novel 4-pole resonators as formant filters. This model has been used to analyse 60 vowels sung at different intensities in order to define a set of functions describing the global variations of parameters along intensity. These functions have been used to modify the intensity of 16 recorded vowels and 8 synthetic vowels generated with Vocaloid. The realism of the transformations performed with our approach has been evaluated by four amateur musicians in comparison to Melodyne for real sounds and to Vocaloid for synthetic sounds. The proposed approach has been proved to achieve more realistic sounds than Melodyne and Vocaloid, especially for loud-to-weak transformations.

In this paper, a method for automatic reduction of dissonance in recorded isolated chords is proposed. Previous approaches address this problem using source separation and note-level processing. In our approach, we manipulate the harmonic structure as a whole in order to avoid beating partials which, according to prior research on dissonance perception, typically produce an unpleasant sound. The proposed system firstly performs a sinusoidal plus residual modeling of the input and analyses the various fundamental frequencies present in the chord. This information is used to create a symbolic representation of the in-tune version of the input according to some musical rules. Then, the partials of the signals are shifted in order to fit the in-tune harmonic structure of the input chord. The input is assumed to contain one isolated chord, with relatively stable fundamental frequencies belonging to the Western chromatic scale. The evaluation has been performed by 31 expert musicians, which have quantified the perceived consonance of six varied, out-of-tune chords in three variants: unprocessed, processed with our system and processed by a state-of-the-art commercial tool (Melodyne Editor). The proposed approach attains an important reduction of the perceived dissonance, showing better performance than Melodyne Editor for most of the cases evaluated.

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: f0curve 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.

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 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.

In this paper, a Successive Interference Cancellation (SIC) receiver typically used for Code Division Multiple Access (CDMA) communications is developed to be used as piano chord detector. The piano notes are modeled as CDMA users by means of the definition of an adequate note pattern. The notes are detected in a successive way and also the estimated interference due to detected notes is removed to improve the detection performance of successive notes. The proposed system attains very good results when compared with other piano chord detectors.

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.

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: detache with and without accent and with and without vibrato, pizzicato, tremolo, spiccato and flageolett-tone. The transcription system is based on a combined analysis of the time domain and frequency domain properties of the music signal.

In this contribution, an automatic system of song edition for Guitar Hero/Frets on Fire is presented. The system performs three fundamental stages: time analysis, frequency analysis and button allocation. The temporal analysis of the musical signal is used to obtain the rhythmic pattern of the song. The frequency analysis is perform on the basis of a Mel filter bank because these filters provide an approximation to the human perception of low and high frequency sounds. The allocation of buttons is done using the rhythmic pattern extracted, the Mel filters outputs and, also, taking into account a selectable difficulty level. The distributions of the buttons obtained with the designed system are similar to the ones found in the predetermined songs of the games.

In this paper, a novel analytical expression for the signal-to-interference ratio (SIR) of the linear multistage partial parallel interference cancellation (PIC) receiver in a UMTS multi-cell system is presented. The SIR expression derived includes the noise effect and the influence of the intra and inter-cell interferences, taking also into account the specific spreading codes of the system users. The evaluation of the analytical SIR expression obtained is very efficient thus allowing to select the optimal parameters for the multistage PIC receiver. Also it can be employed to study and develop admission control (AC) and power control (PC) algorithms

In this letter, we derive a recursive expression for the decision statistic of a linear multistage partial parallel interference cancellation (PIC) receiver for direct-sequence code-division multiple-access systems that allow us to present two closed-form expressions (exact and approximate) for the bit-error probability of this receiver. The bit-error rate is computed in terms of the number of PIC stages, set of cancellation parameters, crosscorrelation between the users' codes, amplitude of the users' signals, and noise power.

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.

We address the problem of stereo matching in the context of Markov random fields (MRFs) and the Bayesian approach. Although MRFs are used as a tool intended to formulate the problem in a way that allows the application of a powerful optimization technique called simulated annealing, the bases for the formulation of the stereo matching problem from a statistical and Markovian point of view are also stated.

In this paper we show the benefits of applying hypothesis testing to the problem of texture segmentation. In our approach, hypothesis testing is used at two different stages that help to reduce the computational burden associated to iterative methods commonly used in image processing. Specifically, hypothesis testing is used to initially estimate the number of regions the image must be divided into, and to determine a set of points that will remain unchanged after the Markovian postprocessing scheme. These fixed points will contribute to reduce the number of iterations required by the Markovian stage and introduce geometry constraints that will reduce the boundary distortion caused by the stochastic procedure.