A method is provided for modifying an image. The method comprises displaying an image, the image comprising a portion of an object; and determining if an edge of the object is in a location within the portion. The method further comprises detecting movement, in a member direction, of an operating member with respect to the edge. The method still further comprises moving, if the edge is not in the location, the object in an object direction corresponding to the detected movement; and modifying, if the edge is in the location, the image in response to the detected movement, the modified image comprising the edge in the location.

Information processing apparatus, information processing method and program

We present a novel system for the automatic extraction of the main melody from polyphonic music recordings. Our approach is based on the creation and characterization of pitch contours, time continuous sequences of pitch candidates grouped using auditory streaming cues. We define a set of contour characteristics and show that by studying their distributions we can devise rules to distinguish between melodic and non-melodic contours. This leads to the development of new voicing detection, octave error minimization and melody selection techniques. A comparative evaluation of the proposed approach shows that it outperforms current state-of-the-art melody extraction systems in terms of overall accuracy. Further evaluation of the algorithm is provided in the form of a qualitative error analysis and the study of the effect of key parameters and algorithmic components on system performance. Finally, we conduct a glass ceiling analysis to study the current limitations of the method, and possible directions for future work are proposed.

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Melody Extraction From Polyphonic Music Signals Using Pitch Contour Characteristics

A method is described which analyzes the basic pattern of beats in a piece of music, the musical meter. The analysis is performed jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level which corresponds to the tempo of a piece, and at the musical measure level. Acoustic signals from arbitrary musical genres are considered. For the initial time-frequency analysis, a new technique is proposed which measures the degree of musical accent as a function of time at four different frequency ranges. This is followed by a bank of comb filter resonators which extracts features for estimating the periods and phases of the three pulses. The features are processed by a probabilistic model which represents primitive musical knowledge and uses the low-level observations to perform joint estimation of the tatum, tactus, and measure pulses. The model takes into account the temporal dependencies between successive estimates and enables both causal and noncausal analysis. The method is validated using a manually annotated database of 474 music signals from various genres. The method works robustly for different types of music and improves over two state-of-the-art reference methods in simulations.

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Analysis of the meter of acoustic musical signals

Sound processing methods and apparatus are provided. A sound capture unit is configured to identify one or more sound sources. The sound capture unit generates data capable of being analyzed to determine a listening zone at which to process sound to the substantial exclusion of sounds outside the listening zone. Sound captured and processed for the listening zone may be used for interactivity with the computer program. The listening zone may be adjusted based on the location of a sound source. One or more listening zones may be pre-calibrated. The apparatus may optionally include an image capture unit configured to capture one or more image frames. The listening zone may be adjusted based on the image. A video game unit may be controlled by generating inertial, optical and/or acoustic signals with a controller and tracking a position and/or orientation of the controller using the inertial, acoustic and/or optical signal.

Methods and apparatus for targeted sound detection and characterization

Comunicacio presentada a la 14th International Society for Music Information Retrieval Conference, celebrada a Curitiba (Brasil) els dies 4 a 8 de novembre de 2013.

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Essentia: An Audio Analysis Library for Music Information Retrieval.

We recently presented a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Nonetheless, compared to modeling waveform directly, ways of effectively handling higher-dimensional outputs, multiple feature streams and regularization become more important with our approach. In this work, we extend our proposed system to include additional components for predicting F0 and phonetic timings from a musical score with lyrics. These expression-related features are learned together with timbrical features from a single set of natural songs. We compare our method to existing statistical parametric, concatenative, and neural network-based approaches using quantitative metrics as well as listening tests.

A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs

This paper introduces the concept of synthesis based on performance sampling. It explains that although sampling has been considered a way to capture and reproduce the sound of an instrument, it should be better considered a way to model the sonic space produced by a performer with an instrument. The paper presents a singing voice synthesizer, pointing out the main issues and complexities emerging along its design. Although the current system is able to generate convincing results in certain situations, there is still much room for improvements, especially in the areas of expression, spectral modeling and sonic space design. However, computer singing is definitely coming close to becoming indistinguishable from human performances

/pdf/synthesis-of-the-singing-voice-by-performance-sampling-and-16zjbhpymy.pdf

Synthesis of the Singing Voice by Performance Sampling and Spectral Models

In this paper we will describe an implementation of a score-performance matching, capable of score following, based on a stochastic approach using Hidden Markov Models.

Score-Performance Matching using HMMs

[Published in the Proceedings of the ICMC 2000] Abstract Time-scale modification of sounds has been a topic of interest in computer music since its very beginning. Many different techniques both in time and frequency domain have been proposed to solve the problem. Some frequency domain techniques yield high-quality results and can work with large modification factors. However, they present some artifacts, like phasiness, loss of attack sharpness and loss of stereo image. In this paper we propose a new frequency domain approach for an automatic near-lossless time stretching of audio.

Automatic Technique in Frequency Domain for Near-Lossless Time-Scale Modification of Audio

An apparatus is constructed for converting an input voice signal into an output voice signal according to a target voice signal. In the apparatus, an input device provides the input voice signal composed of original sinusoidal components and original residual components other than the original sinusoidal components. An extracting device extracts original attribute data from at least the sinusoidal components of the input voice signal. The original attribute data is characteristic of the input voice signal. A synthesizing device synthesizes new attribute data based on both of the original attribute data derived from the input voice signal and target attribute data being characteristic of the target voice signal composed of target sinusoidal components and target residual components other than the sinusoidal components. The target attribute data is derived from at least the target sinusoidal components. An output device operates based on the new attribute data and either of the original residual component and the target residual component for producing the output voice signal.

Jordi Bonada

Papers

A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs

Synthesis of the Singing Voice by Performance Sampling and Spectral Models

Score-Performance Matching using HMMs

Automatic Technique in Frequency Domain for Near-Lossless Time-Scale Modification of Audio

Voice converter with extraction and modification of attribute data