高橋 徹

我々は，可聴周波数帯域での振幅変調に基づく信号伝送システムの設計と実装について述べる。典型的な振幅変調に基づく信号伝送システムでは，信号は，非常に高い周波数によって変調される。我々のシステムは，２から20000Hzの間の可聴信号のような非常に低い周波数を用いる。我々は，可聴帯域で信号を伝送可能であることを実験で示した。We describe a design and an implementation of a signal transmission system based on the amplitude modulation in audible frequency bands. In conventional signal transmission system based on the amplitude modulation, signal is modulated by very high frequency.Our system uses very low frequency, such as audible signal between 2 and 20000Hz. We experimentally show that it is possible to transmit based on the audible bands.

本稿では,GPSを用いた路面電車の位置を通知するシステムの開発と評価について述べ,設計思想を示す.最も重要な設計思想は,容易に入手可能な汎用品を用いてシステムを構成する点である.何故ならば,バスやタクシーや電車のような他の交通機関への導入が推進されることを狙っているためである.本コンセプトに基づくプロトタイプシステムを開発し,阪堺電気軌道で試験サービスを実施し評価した.位置情報を地図上に表す時の測位値をそのまま地図上に表示する替わりに,推定誤差軽減のためマップマチングアルゴリズムを用いて補正した.またマップマッチングアルゴリズムに用いるアンカーポイント間の間隔について評価している.以上の評価に基づきシステムをチューニングした結果,最大誤差で100mで,情報表示までの遅延を3秒程度に抑えることができた.

本稿では，歌声と朗読音声を識別するシステムについて述べる．入力は無雑音音声，出力は歌声と朗読音声それぞれの尤度 （連続値） である．従来，スペクトル包絡 （MFCC） と基本周波数 （F0） の時間変化に基づいた識別システムが報告されている．これらの特徴量に基づく識別器に，スペクトル変化量のピーク間隔という，音素継続時間に関連する特徴量に基づく識別器を加え，入力音声長に応じて各識別器への重みを変化させた．実験の結果，従来システムでは1秒の音声に対し 86.7% の精度であったのに対し，本システムでは 90.2% という結果を得た．本システムが実時間で動作するデモアプリケーションについても述べる．In this paper we describe a system that discriminates between singing and speaking voices. Given a clean speech signal, it outputs the likelihood of each of the singing and speaking voices. Previous systems use temporal transition of spectral envelope (MFCC) and fundamental frequency (F0) as discrimina- tion features. Our system adds peak interval of spectral change as a phoneme duration feature and weights these features according to the duration of the input speech signal. Experimental results with one-second speech signal show that our system achieves 90.2 % accuracy compared to 86.7 % with previous systems. We also describe a real-time application demonstrating our system.

We present a method of blind source separation (BSS) for speech signals using a complex extension of infinite sparse factor analysis (ISFA) in the frequency domain. Our method is robust against delayed signals that usually occur in real environments, such as reflections, short-time reverberations, and time lags of signals arriving at microphones. ISFA is a conventional non-parametric Bayesian method of BSS, which has only been applied to time domain signals because it can only deal with real signals. Our method uses complex normal distributions to estimate source signals and mixing matrix. Experimental results indicate that our method outperforms the conventional ISFA in the average signal-to-distortion ratio (SDR). © 2012 Springer-Verlag.

This paper focuses on blind speech separation in under-determined conditions, that is, in the case when there are more sound sources than microphones. We introduce a sound source model based on the Gaussian mixture model (GMM) to represent a speech signal in the time-frequency domain, and derive rules for updating the model parameters using the auxiliary function method. Our GMM sound source model consists of two kinds of Gaussians: sharp ones representing harmonic parts and smooth ones representing nonharmonic parts. Experimental results reveal that our method outperforms the method based on non-negative matrix factorization (NMF) by 0.7dB in the signal-to-distortion ratio (SDR), and by 1.7dB in the signal-to-interference ratio (SIR). This means that our method effectively removes interference coming from other talkers. © 2012 Springer-Verlag.

合奏において、ビートトラッキングは動作タイミングの取得の基礎となる技術である。ギターとの合奏において、ビートトラッキングは演奏テンポの揺らぎや裏拍ビートを含む多様なリズムへの頑健性、つまり(1)テンポと(2)音符長の両方の変動に対する追従性が要求される。しかし従来の手法では両立できなかった。本研究では視聴覚情報統合により、両者の変動追従性向上を実現する。(1)の問題にはSTPMという聴覚情報を用いた手法を適用する。(2)の問題はギター演奏動作の周期性を利用し手の位置情報を取得、それとSTPMで得られる信頼度関数とに粒子フィルタを適用することで解決する。

本研究は複数の環境音を認識し，その結果を対話に役立てることができるロボット聴覚システムの開発を目標とする．ロボット聴覚における環境音認識の課題は:(1) 主にロボット自身からの音による多雑音環境下での認識，(2) 音源分離処理によるスペクトル歪みに頑健な特徴量の必要性，である．本稿では同時に発生する二つの環境音を定位・分離し，個別の信号を，信号中の目立った特徴のみを抽出可能なGaborウェーヴレットによる matching pursuit (MP) を用いた認識実験を行う．実験結果から，音源の特性に依存するものの，MPで抽出した特徴量の雑音頑健性が示された．

This paper presents the design and implementation of selectable sound separation functions on the telepresence system "Texai" using the robot audition software "HARK." An operator of Texai can "walk" around a faraway office to attend a meeting or talk with people through video-conference instead of meeting in person. With a normal microphone, the operator has difficulty recognizing the auditory scene of the Texai, e.g., he/she cannot know the number and the locations of sounds. To solve this problem, we design selectable sound separation functions with 8 microphones in two modes, overview and filter modes, and implement them using HARK's sound source localization and separation. The overview mode visualizes the direction-of-arrival of surrounding sounds, while the filter mode provides sounds that originate from the range of directions he/she specifies. The functions enable the operator to be aware of a sound even if it comes from behind the Texai, and to concentrate on a particular sound. The design and implementation was completed in five days due to the portability of HARK. Experimental evaluations with actual and simulated data show that the resulting system localizes sound sources with a tolerance of 5 degrees.

Our goal is to achieve a robot audition system that is capable of recognizing multiple environmental sounds and making use of them in human-robot interaction. The main problems in environmental sound recognition in robot audition are: (1) recognition under a large amount of background noise including the noise from the robot itself, and (2) the necessity of robust feature extraction against spectrum distortion due to separation of multiple sound sources. This paper presents the environmental recognition of two sound sources fired simultaneously using matching pursuit (MP) with the Gabor wavelet, which extracts salient audio features from a signal. The two environmental sounds come from different directions, and they are localized by multiple signal classification and, using their geometric information, separated by geometric source separation with the aid of measured head-related transfer functions. The experimental results show the noise-robustness of MP although the performance depends on the properties of the sound sources. © 2011 Springer-Verlag.