Nippon Telegraph and Telephone Corporation

The University of Tokyo

Roland Corporation

Toudai Tlo, Ltd.

Meiji University

Nippon Telegraph & Telephone Co.

Shigeki Sagayama

Takehiro Moriya

Satoshi Takahashi

Yutaka Kamamoto

Noboru Harada

Takuya Nishimoto

Junichi Takahashi

Osamu Mizuno

Akihiro Nagata

Jun-ichi Takahashi

Yoshikazu Yamaguchi

Tokio Takahashi

Kohei Hayashi

The automatic performance device includes a storage part for storing a plurality of performance patterns, a performance part for playing a performance on the basis of a performance pattern stored in the storage part, an input part for inputting performance information through an input device that receives a performance operation of a user, and a selection part for selecting a performance pattern being a maximum likelihood estimation from the plurality of performance patterns stored in the storage part on the basis of the performance information input to the input part.

Automatic performance device and automatic performance method

A method and apparatus multiplies a past sample a time lag &#x3c4; older than a current sample by a quantized multiplier &#x3c1;' on a frame by frame basis, subtracts the multiplication result from the current sample, codes the subtraction result, and codes the time lag using a fixed-length coder if the multiplier &#x3c1;&#x2032; is smaller than 0.2 or if information about the previous frame is unavailable, or codes the time lag using a variable-length coder if &#x3c1;&#x2032; is not smaller than 0.2. A multiplier &#x3c1; is coded by a multiplier coder and the multiplier &#x3c1;&#x2032; obtained by decoding the multiplier &#x3c1; is outputted. The process is performed for each frame.

Method, apparatus, program, and recording medium for long-term prediction coding and long-term prediction decoding

Method, apparatus, program and recording medium for long-term prediction coding and long-term prediction decoding

In difference coding, each of a first to M-th channel signals is divided into frames and independent energy of every channel signal and difference energy of difference signals between all channel signals are calculated for each frame. In ascending order of energy, if a signal corresponding to an energy value is independent signal, it is determined that independent coding should be used for the signal. If the signal is a difference signal and the type of coding for one of channel signals has been determined, it is determined that the other should be difference-coded using the former channel signal as a reference signal. If the type of coding for neither of the channel signals has been determined, it is determined that one of them should be independently coded and the other should be difference-coded using the former channel signal as a reference signal.

Multi-channel signal encoding method, decoding method, device thereof, program, and recording medium thereof

An object of the present invention is to efficiently perform weighted difference coding of two or more signals. Determination is made as to which of independent coding and weighted difference coding is to be used for each channel so that the total energy of the channel signals and weighted difference signals is minimized. A weighted difference signal is generated on the basis of the determination and a reference signal (parent) and a weight is generated as auxiliary codes, the difference signal is treated as an input channel signal, and the process of coding determination and difference signal and auxiliary code generation is repeated. The difference signal generated at the last iteration of the process and a signal to be coded by independent coding are compressive coded and the auxiliary codes generated at the iterations of the process are coded and outputted.

Multichannel signal encoding method, its decoding method, devices for these, program, and its recording medium

A pattern recognition scheme using probabilistic models that are capable of reducing a calculation cost for the output probability while improving a recognition performance even when a number of mixture component distributions of respective states is small, by arranging distributions with low calculation cost and high expressive power as the mixture component distribution. In this pattern recognition scheme, a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal is calculated, where the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions. Then, a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models is outputted as a recognition result.

Pattern recognition scheme using probabilistic models based on mixtures

A model adaptation scheme in the pattern recognition, which is capable of realizing a fast, real time model adaptation and improving the recognition performance. This model adaptation scheme determines a change in a parameter expressing a condition of pattern recognition and probabilistic model training between an initial condition at a time of acquiring training data used in obtaining a model parameter of each probabilistic model and a current condition at a time of actual recognition. Then, the probabilistic models are adapted by obtaining a model parameter after a condition change by updating a model parameter before a condition change according to the determined change, when the initial condition and the current condition are mismatching. The adaptation processing uses a Taylor expansion expressing a change in the model parameter in terms of a change in the parameter expressing the condition.

Scheme for model adaptation in pattern recognition based on Taylor

Training data is LPC analyzed to obtain a feature parameter vector sequence, which is subjected to Viterbi segmentation using reference phoneme models to separate phonemes. Each piece of phoneme data is used to estimate a mean vector of the corresponding reference phoneme model by a maximum a posteriori estimation method. The adapted phoneme model and the corresponding reference phoneme model are used to estimate a mean vector for an unadapted phoneme model through interpolation by a vector field smoothing method. Alternatively, the mean vector of the adapted phoneme model is further smoothed by the vector field smoothing method. By this, an adapted model is obtained which has, as its parameters, the mean vector obtained for each phoneme and other corresponding parameters.

Adaptive training method for pattern recognition

One-dimensional normal distributions in respective dimensions of a continuous multi-dimensional normal distribution of each state of HMMs representing speech units mean and variance values are tied among similar one-dimensional distributions. As a result, the total number of normal distributions for representing the model is reduced without degrading recognition performance.

Pattern recognition method

In method for detecting a speech period in a high-noise environment, the variation in the spectrum of an input signal per unit time is calculated over an analysis frame period, and when the frequency of spectrum variation falls in a predetermined range, the input signal of that frame is decided to be a speech signal.

Growing community of inventors

Bunkyo-ku, Japan

Shigeki Sagayama