JP4986393B2 - 雑音低減値に対する推定値を決定する方法 - Google Patents
雑音低減値に対する推定値を決定する方法 Download PDFInfo
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- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
- G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
- G10L2021/02165—Two microphones, one receiving mainly the noise signal and the other one mainly the speech signal
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Description
図4および5は、クリーンな音声の推定値を生成するために補正ベクトルに依拠する本発明の2つの実施形態用の、ステレオ補正ベクトルをトレーニングすることについてのブロック図およびフロー図を提供する。
p(bt|s)=N(bt;μb,Γb) 式3
としてモデル化され、平均値μbおよび分散Γbは期待値最大化(EM)アルゴリズムを用いてトレーニングされており、各反復は、以下のステップからからなる。
補正ベクトルおよび雑音推定値に基づいて、雑音のある音声信号中の雑音を低減するシステムおよび方法を、図6のブロック図および図7のフロー図にそれぞれ示す。
Σx|b=JΣJT 式11
である。
図8は、本発明においてクリーンな音声値を推定する補助システムのブロック図を提供する。図8のシステムは、クリーンな音声値の推定値が気導マイクロホンまたは雑音モデルを必要とせずに形成されること以外は、図6のシステムと同様である。
クリーンな音声信号の推定値を生成する代替技術を、図9のブロック図および図10のフロー図に示す。具体的には、図9および10の実施形態は、補助センサを用いて音声信号に対するピッチを識別し、次いで、このピッチを用いて雑音のある気導マイクロホンの信号を高調波成分およびランダム成分に分解することによって、クリーン音声推定値を決定する。したがって、雑音のある信号は、以下のように表される。
上式で、yは雑音のある信号であり、yhは高調波成分であり、yrはランダム成分である。高調波成分およびランダム成分の、重みづけ和は、雑音が低減された音声信号を表す、雑音が低減された特徴ベクトルを形成するのに用いられる。
y=Ab 式18
のように書き換えられ、上式で、yは、雑音のある音声信号のN個のサンプルのベクトルであり、Aは、
A=[AcosAsin] 式19
によって与えられるN×2Kの行列であり、式19の要素は
Acos(k,t)=cos(kω0t) Asin(k,t)=sin(kω0t) 式20
であり、bは、
bT=[a1a2...akb1b2...bk] 式21
によって与えられる2K×1のベクトルである。この場合、振幅係数に対する最小二乗解は、
yr=y−yh 式24
のように計算される。
110 コンピュータ
120 処理装置
121 システムバス
130 システムメモリ
131 ROM
132 RAM
133 BIOS
134 オペレーティングシステム
135 アプリケーションプログラム
136 他のプログラムモジュール
137 プログラムデータ
140 固定の不揮発性メモリインターフェイス
144 オペレーティングシステム
145 アプリケーションプログラム
146 他のプログラムモジュール
147 プログラムデータ
150 取外し可能不揮発性メモリインターフェイス
160 ユーザ入力インターフェイス
161 ポインティングデバイス
162 キーボード
163 マイクロホン
170 ネットワークインターフェイス
171 ローカルエリアネットワーク
172 モデム
173 ワイドエリアネットワーク
180 リモートコンピュータ
185 リモートアプリケーションプログラム
190 ビデオインターフェイス
191 モニタ
195 出力周辺インターフェイス
196 プリンタ
197 スピーカ
200 モバイル装置
202 プロセッサ(マイクロプロセッサ)
204 メモリ
208 通信インターフェイス
214 アプリケーション
216 オブジェクトストア
Claims (6)
- 雑音が低減された音声信号の一部を表す雑音低減値に対する推定値を決定する方法であって、
気導マイクロホン以外の補助センサを用いて補助センサ信号を生成するステップと、
前記補助センサ信号を少なくとも1つのケプストラム領域における補助センサベクトルに変換するステップと、
前記補助センサベクトルに複数の補正ベクトルの重みづけ和を加算して、ケプストラム領域における前記雑音低減値に対する前記推定値を形成するステップであって、各補正ベクトルは、類似度の高い前記補助センサベクトルをグループ化した混合成分に対応し、補正ベクトルに適用される各重みは、前記補助センサベクトルを与えられた前記補正ベクトルの混合成分の確率に基づく、ステップと、
気導マイクロホン信号を生成するステップと、
前記気導マイクロホン信号をパワースペクトル領域における気導ベクトルに変換するステップと、
雑音値を推定するステップと、
前記気導ベクトルから前記雑音値を減算して、パワースペクトル領域における気導推定値を形成するステップと、
前記雑音低減値に対する前記推定値をケプストラム領域からパワースペクトル領域に変換するステップと、
パワースペクトル領域において、前記気導推定値および前記雑音低減値に対する前記推定値を結合して、前記雑音低減値に対するパワースペクトル領域における改善された推定値を形成するステップと
を備えることを特徴とする方法。 - 前記補助センサ信号を生成するステップは、骨伝導マイクロホンを用いて前記補助センサ信号を生成するステップを含むことを特徴とする請求項1に記載の方法。
- 前記各ステップを通して補正ベクトルをトレーニングするステップをさらに含み、該ステップは、
補助センサトレーニング信号を生成するステップと、
前記補助センサトレーニング信号を補助センサトレーニングベクトルに変換するステップと、
クリーンな気導マイクロホントレーニング信号を生成するステップと、
前記クリーン気導マイクロホントレーニング信号を気導トレーニングベクトルに変換するステップと、
前記補助センサトレーニングベクトルおよび前記気導トレーニングベクトルの間の差分を用いて、前記補正ベクトルを形成するステップと
を含むことを特徴とする請求項1に記載の方法。 - 前記補正ベクトルをトレーニングするステップは、前記複数の混合成分それぞれに対して別個の補正ベクトルをトレーニングするステップをさらに含むことを特徴とする請求項3に記載の方法。
- 前記雑音低減値に対する前記改善された推定値を用いて、フィルタを形成するステップをさらに含むことを特徴とする請求項1に記載の方法。
- 気導マイクロホン以外の第2の補助センサを用いて第2の補助センサ信号を生成するステップと、
前記第2の補助センサ信号を少なくとも1つの第2の補助センサベクトルに変換するステップと、
前記第2の補助センサベクトルに補正ベクトルを加算して、前記雑音低減値に対する第2の推定値を形成するステップと、
前記雑音低減値に対する前記推定値を、前記雑音低減値に対する前記第2の推定値と結合して、前記雑音低減値に対する改善された推定値を形成するステップと
をさらに含むことを特徴とする請求項1に記載の方法。
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JP2011153225A Division JP5247855B2 (ja) | 2003-11-26 | 2011-07-11 | 複数感知の音声強調のための方法および機器 |
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JP2011153225A Expired - Fee Related JP5247855B2 (ja) | 2003-11-26 | 2011-07-11 | 複数感知の音声強調のための方法および機器 |
JP2011153227A Expired - Fee Related JP5147974B2 (ja) | 2003-11-26 | 2011-07-11 | 複数感知の音声強調のための方法および機器 |
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JP2011153227A Expired - Fee Related JP5147974B2 (ja) | 2003-11-26 | 2011-07-11 | 複数感知の音声強調のための方法および機器 |
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US (1) | US7447630B2 (ja) |
EP (2) | EP1536414B1 (ja) |
JP (3) | JP4986393B2 (ja) |
KR (1) | KR101099339B1 (ja) |
CN (2) | CN101887728B (ja) |
AU (1) | AU2004229048A1 (ja) |
BR (1) | BRPI0404602A (ja) |
CA (2) | CA2485800C (ja) |
MX (1) | MXPA04011033A (ja) |
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CN101887728B (zh) | 2011-11-23 |
US7447630B2 (en) | 2008-11-04 |
EP1536414A3 (en) | 2007-07-04 |
EP1536414A2 (en) | 2005-06-01 |
CA2485800C (en) | 2013-08-20 |
JP5247855B2 (ja) | 2013-07-24 |
RU2004131115A (ru) | 2006-04-10 |
CN1622200B (zh) | 2010-11-03 |
MXPA04011033A (es) | 2005-05-30 |
EP1536414B1 (en) | 2012-05-23 |
JP5147974B2 (ja) | 2013-02-20 |
JP2005157354A (ja) | 2005-06-16 |
RU2373584C2 (ru) | 2009-11-20 |
CN1622200A (zh) | 2005-06-01 |
BRPI0404602A (pt) | 2005-07-19 |
CA2786803C (en) | 2015-05-19 |
KR101099339B1 (ko) | 2011-12-26 |
CN101887728A (zh) | 2010-11-17 |
KR20050050534A (ko) | 2005-05-31 |
US20050114124A1 (en) | 2005-05-26 |
AU2004229048A1 (en) | 2005-06-09 |
JP2011203759A (ja) | 2011-10-13 |
CA2786803A1 (en) | 2005-05-26 |
JP2011209758A (ja) | 2011-10-20 |
EP2431972A1 (en) | 2012-03-21 |
EP2431972B1 (en) | 2013-07-24 |
CA2485800A1 (en) | 2005-05-26 |
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