JP6312110B2 - 信号の1つ又は複数の成分を区別する方法 - Google Patents
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Description
機械学習において、スペクトルクラスタリングは、画像及び音響のセグメンテーションのために用いられている。スペクトルクラスタリングは、信号の要素の特徴間のローカルアフィニティ尺度(local affinity measure)を用い、正規化されたアフィニティ行列のスペクトル分解を用いて様々な目的関数を最適化する。k平均法等の従来の集中型クラスタリングと対照的に、スペクトルクラスタリングは、点が中心プロトタイプの周りに密にクラスタリングされることを必要とせず、クラスターが連結されたサブグラフを形成することを条件として、任意のトポロジのクラスターを決定することができるという利点を有する。用いられるペアワイズカーネル関数の局所形式に起因して、難解なスペクトルクラスタリング問題において、アフィニティ行列は、集中型クラスタリングに直接適用可能でないスパースなブロック対角構造を有する。これは、ブロック対角アフィニティ構造が密であるときに良好に機能する。スペクトルクラスタリングの、強力であるが計算的に複雑な固有空間変換ステップは、事実上、ブロック構造を「膨らませる」ことによってこれに対処し、それによって、連結された成分は、集中型クラスタリングの前に密なブロックになる。
時間領域音響信号101はxであり、解析特徴110は、要素iによってインデックス付けされたベクトルXi=gi(x),n∈{1,...,N}の形態であり、ここで、iは時間周波数インデックス(t,f)とすることができ、ここで、tは音響信号のフレームをインデックス付けし、fは周波数を表す。対応する時間周波数ビンにおける複素スペクトルの値はXi=Xt,fである。
なお、DNNは、畳み込みニューラルネットワークとすることができる。また、関連記述子と1つ又は複数の成分との間の関連付けは、ガウス混合モデルを用いて推定されるようにすることができる。また、信号の1つ又は複数の成分は、オブジェクトを認識するために用いられるようにすることができる。また、別個の信号のうちの1つ又は複数は、音声認識システムにおいて、話者が発話する単語を認識するように処理されるようにすることができる。
Claims (15)
- 信号の1つ又は複数の成分を区別する方法であって、
前記信号は音響信号であり、
前記方法は、
音響センサーを用いて環境から前記信号を取得するステップと、
前記信号を処理して、1組の解析特徴を推定するステップであって、各解析特徴は、前記信号の要素を定義し、前記信号の部分を表す特徴値を有する、ステップと、
前記信号を処理して前記信号の入力特徴を推定するステップと、
深層ニューラルネットワーク(DNN)を用いて前記入力特徴を処理して、関連記述子を前記信号の各要素に割り当てるステップであって、異なる要素の前記関連記述子間の類似度は、前記要素によって表される前記信号の前記部分が前記信号の単一の成分に属する度合いに関係する、ステップと、
前記関連記述子間の類似度を処理して、前記信号の前記要素と前記信号内の1つ又は複数の成分との間の対応関係を推定するステップと、
前記対応関係を用いて前記信号を処理して、前記信号の前記1つ又は複数の成分の前記部分を区別するステップと、
を含み、
前記ステップはプロセッサにおいて実行される、
方法。 - 前記信号は、前記信号の前記1つ又は複数の成分に対応する前記要素の強度を変更するように処理される、
請求項1に記載の方法。 - 前記DNNはリカレントニューラルネットワークである、
請求項1に記載の方法。 - 前記ニューラルネットワークは畳み込みニューラルネットワークである、
請求項1に記載の方法。 - 前記関連記述子と前記1つ又は複数の成分との間の関連付けは、K平均クラスタリングを用いて推定される、
請求項1に記載の方法。 - 前記関連記述子と前記1つ又は複数の成分との間の関連付けは、ガウス混合モデルを用いて推定される、
請求項1に記載の方法。 - 前記関連記述子と前記1つ又は複数の成分との間の関連付けは、特異値分解を用いて推定される、
請求項1に記載の方法。 - 前記関連記述子間の関連付けが処理されてグラフが形成され、前記信号に対しグラフベースの信号処理が行われる、
請求項1に記載の方法。 - 前記信号は、マルチチャネル音響信号を含み、チャネル間のタイミング関係を用いて、前記要素のための前記記述子が推定される、
請求項1に記載の方法。 - 前記信号の前記成分のうちの1つ又は複数は音声であり、前記信号の前記処理は、1つ又は複数の音声信号に対応する別個の信号を生成する、
請求項1に記載の方法。 - 前記信号の前記1つ又は複数の成分は、オブジェクトを認識するために用いられる、
請求項1に記載の方法。 - 別個の信号のうちの1つ又は複数は、音声認識システムにおいて、話者が発話する単語を認識するように処理される、
請求項11に記載の方法。 - 前記1つ又は複数の成分は、類似度の異なる尺度を用いて区別され、前記対応関係が推定される、
請求項1に記載の方法。 - 前記1つ又は複数の成分は、階層に組織化され、前記階層の1つ又は複数のレベルにおける対応関係は、前記類似度の異なる尺度を用いて推定される、
請求項13に記載の方法。 - 前記DNNは、トレーニングデータを用いて、前記関連記述子を生成するように最適化され、それによって、前記関連記述子間の類似度の前記処理をして、前記信号の前記要素と前記信号内の1つ又は複数の成分との間の対応関係を推定することにより、前記信号の前記成分を区別する際の誤りが低減する、
請求項1に記載の方法。
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PCT/JP2016/070355 WO2017007035A1 (en) | 2015-07-07 | 2016-07-05 | Method for distinguishing one or more components of signal |
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