JP7271674B2 - ネットワークマイクロフォンデバイスのノイズ分類による最適化 - Google Patents
ネットワークマイクロフォンデバイスのノイズ分類による最適化 Download PDFInfo
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Description
音声による制御は、ワイヤレスオーディオ再生装置、照明装置、ホームオートメーション装置(サーモスタット、ドアロック等)など、通信ネットワークに接続されたスマート家電や装置を含む「スマート」ホームにおいて有益である。いくつかの用途においては、スマートホームデバイスを制御するため、ネットワークマイクロフォンデバイスが用いられることができる。
図1Aおよび図1Bは、ここに開示された1つまたは複数の実施形態が実施され得るメディア再生システム100(または「MPS100」)の構成例を示す。まず、図1Aを参照すると、図示されているMPS100は、複数の部屋と空間を有する例示的な家庭環境に関連付けられており、これらは総称して「家庭環境」、「スマートホーム」、または「環境101」とも言う。環境101は、マスターバスルーム101a、マスターベッドルーム101b(ここでは「ニックの部屋」と呼ぶ)、セカンドベッドルーム101c、ファミリールームまたはデン101d、オフィス101e、リビングルーム101f、ダイニングルーム101g、キッチン101h、および屋外パティオ101iを含み、いくつかの部屋、スペース、および/または再生ゾーンを有する家庭からなる。以下では、家庭環境の下での特定の実施形態や例を説明するが、ここで説明する技術は他のタイプの環境でも実施可能である。いくつかの実施形態では、例えば、MPS100は、1つ以上の商業環境(例えば、レストラン、モール、空港、ホテル、小売店などの店舗)、1つ以上の車両(例えば、スポーツユーティリティビークル、バス、車、船、ボート、飛行機)、複数の環境(例えば、家庭環境と車両環境の組み合わせ)、および/または、マルチゾーンオーディオが望ましいと思われる別の適切な環境で実施することができる。
図2Aは、図1Aおよび図1BのMPS100の再生装置102の1つの特定の側面を示す機能ブロック図である。図示されているように、再生装置102は、それぞれが以下でさらに詳細に説明される様々な構成要素を含み、再生装置102の様々な構成要素は、システムバス、通信ネットワーク、又は何らかの他の接続機構を介して、互いに動作可能に組合せされている。図2Aの図示された例では、再生装置102は、図1Aに示されたNMD103の一例の様に、NMDの機能をサポートするコンポーネントを含むため、「NMD搭載」再生装置と呼ぶことがある。
図3A~3Eは、再生装置の例示的な構成を示す。最初に図3Aを参照すると、いくつかの例示的な実施形態では、単一の再生装置がゾーンに属してもよい。例えば、パティオの再生装置102c(図1A)は、ゾーンAに属していてもよい。以下に説明するいくつかの実施形態では、複数の再生機器を「結合(bond)」して「結合ペア」を形成し、それらが一緒になって1つのゾーンを形成することができる。例えば、図3Aで「ベッド1」と名付けられた再生装置102f(図1A)と、図3Aで「ベッド2」と名付けられた再生装置102g(図1A)とを結合して、ゾーンBを形成してもよい。結合された再生装置のそれぞれは、異なる再生責任(例えば、チャンネル責任)を持つ。後述する別の実施形態では、複数の再生装置を統合して1つのゾーンを形成することができる。統合された再生装置102d、102mには、特に異なる再生責任が割り当てられていなくてもよい。すなわち、統合された再生装置102d、102mは、同期してオーディオコンテンツを再生することは勿論であるが、それぞれが統合されていない場合と同様にオーディオコンテンツを再生してもよい。
図4Aは、図1AのMPS100のコントローラデバイス104のうちの選択された1つの例を示す機能ブロック図である。このようなコントローラデバイスは、ここでは「コントロールデバイス」または「コントローラ」と呼ぶ。図4Aに示すコントローラデバイスは、プロセッサ412、プログラムソフトウェア414を格納するメモリ413、少なくとも1つのネットワークインターフェース424、および1つまたは複数のマイクロフォン422など、上述したネットワークデバイスの特定のコンポーネントと一般的に類似したコンポーネントを含む。一例として、コントローラデバイスは、MPS100の専用コントローラであってもよい。別の例では、コントローラデバイスは、例えば、iPhone(登録商標)、iPad(登録商標)、その他のスマートフォン、タブレット、ネットワークデバイス(例えば、PCやMac(登録商標)などのネットワークコンピュータ)など、メディア再生システムのコントローラーアプリケーションソフトウェアがインストールされているネットワークデバイスであってもよい。
ソース領域448内のオーディオソースは、選択された再生ゾーンまたはゾーングループによってオーディオコンテンツが取得され、再生され得るオーディオコンテンツソースである。ゾーンまたはゾーングループ内の1つまたは複数の再生装置は、利用可能なさまざまなオーディオコンテンツソースからオーディオコンテンツを(例えば、オーディオコンテンツに対応するURIまたはURLに従って)再生のために取得するように構成されている。ある例では、オーディオコンテンツは、再生装置によって、対応するオーディオコンテンツソースから直接(例えば、ラインイン接続を介して)取得され得る。別の例では、オーディオコンテンツは、1つまたは複数の他の再生装置またはネットワーク装置を介してネットワーク上の再生装置に提供される。以下で詳しく説明するように、ある実施形態では、オーディオコンテンツは、1つまたは複数のメディアコンテンツサービスによって提供され得る。
図5は、本開示の実施形態に基づいて構成されたNMD503を機能的に示すブロック図である。NMD503は、ボイスキャプチャコンポーネント(「VCC」、または総称して「ボイスプロセッサ560」)、ウェイクワードエンジン570、および少なくとも1つの音声抽出器572を含み、これらの各々はボイスプロセッサ560に動作可能に結合されている。NMD503は、上述したマイクロフォン222および少なくとも1つのネットワークインタフェース224をさらに含み、また、明確にするために図5には示されていない、オーディオアンプ、インタフェースなどの他のコンポーネントを含んでもよい。
動作において、NMDは、交通、器具(例えば、ファン、流し、冷蔵庫など)、建設、干渉するスピーチなどの様々な異なるタイプのノイズにさらされる可能性がある。このようなノイズが存在する場合にキャプチャされたオーディオ入力をより適切に分析するには、オーディオ入力のノイズを分類することが望まれる。異なるノイズ源は異なるサウンドを生成し、それらの異なるサウンドはサウンドメタデータ(例えば、周波数応答、信号レベルなど)について異なった特性を有する。さまざまなノイズソースに関連付けられたさまざまなサウンドメタデータには、あるノイズソースを別のノイズソースから区別するシグネチャ(個性)を認めることができる。したがって、サウンドメタデータを分析し、異なるシグニチャを識別することにより、異なるノイズ源を分類することができる。図7は、4つのノイズ源に関連して分析されたサウンドメタデータを示す。左上のプロットは、NMDから3フィート離れ、高い位置に設定されたファンのノイズであり、右上のプロットは環境ノイズであり、左下のプロットは、NMDから3フィート離れたランニングシンク(流し台)のノイズであり、右下のプロットは、NMDから3フィート離れたところにある料理の焼ける音である。ある実施形態では、プロットに表示されるこれらのシグネチャは、信号の主要成分の分析を使用して生成することができる。図10~図13により詳細に説明するように、種々のNMDから収集されたデータは、可能な周波数応答スペクトルの全体的な分布を提供することができる。一般的に、主要成分の分析(PCA)を用いて、すべてのフィールドデータの分散を記述する直交基底を見つけることができる。この固有空間は、図7のプロットに示されている等高線に反映されている。プロットにおける各ドットは、固有空間に投影される既知のノイズ値(たとえば、注目のノイズ源にさらされたNMDからの単一の周波数応答スペクトル)を表わす。図7に示すように、これらの既知のノイズ値は、固有空間に投影されるとクラスター化され、さまざまなノイズ源に対してさまざまに異なるシグネチャ分布を生成する。以下でより詳細に説明するように、このノイズの分類を使用して、NMDのパフォーマンスを向上させることができる。
X = USVT
以上の説明では、とりわけ、ハードウェア上で実行されるファームウェアおよび/またはソフトウェアを含む、さまざまな例示的なシステム、方法、装置、および製造品を開示した。上記の説明は単なる例示であり、限定的なものと解されるべきではない。例えば、ファームウェア、ハードウェア、および/またはソフトウェアの側面またはコンポーネントのいずれかまたはすべてが、ハードウェアのみ、ソフトウェアのみ、ファームウェアのみ、またはハードウェア、ソフトウェア、および/またはファームウェアの任意の組み合わせで具現化できることが企図されている。したがって、これらの例は、そのようなシステム、方法、装置、および製造品を実装する唯一の方法ではない。
1つ以上のマイクロフォンを介してサウンドを検出するステップと、検出されたサウンドに基づいてNMDの第1バッファにサウンドデータを取り込むステップと、NMDを介してサウンドデータを分析してトリガーイベントを検出するステップと、NMDの少なくとも第2バッファにサウンドデータに関連するメタデータを取り込むステップと、トリガーイベントを検出した後、サウンドデータ内のノイズを分類するためメタデータを分析するステップと、分類されたノイズに基づいてNMDの少なくとも1つの性能パラメータを修正するステップ、
ことを特徴とする再生装置。
1つ以上のマイクロフォンを介してサウンドを検出するステップと、検出されたサウンドに基づいてNMDの第1バッファにサウンドデータを取り込むステップと、NMDを介してサウンドデータを分析してトリガーイベントを検出するステップと、NMDの少なくとも第2バッファにサウンドデータに関連するメタデータを取り込むステップと、トリガーイベントを検出した後、サウンドデータ内のノイズを分類するためメタデータを分析するステップと、分類されたノイズに基づいてNMDの少なくとも1つの性能パラメータを修正するステップ、
ことを特徴とする有形、非一過性のコンピュータ可読媒体。
Claims (15)
- ネットワークマイクロフォンデバイス(NMD)の1つ以上のマイクロフォンを介してサウンドを検出するステップと、
検出されたサウンドに基づいてNMDの第1バッファにサウンドデータをキャプチャするステップと、
トリガーイベントを検出するためにNMDを介してサウンドデータを分析するステップと、
NMDの少なくとも第2バッファにサウンドデータに関連するメタデータであって、このメタデータから元のサウンドデータには復元できないメタデータ、をキャプチャするステップと、
トリガーイベントを検出した後、
サウンドデータを音声アシスタントサービスに送るステップと、
サウンドデータ内のノイズを分類するためメタデータの分析を発動するステップと、
分類されたノイズに基づいてNMDの少なくとも1つの性能パラメータを修正するステップと
を含む方法。 - 請求項1に記載の方法であって、サウンドデータ内のノイズを分類するためにメタデータを分析することは、メタデータを、既知のノイズイベントに関連する参照メタデータと比較することである、ことを特徴とする方法。
- 請求項2に記載の方法であって、メタデータを参照メタデータと比較することは、メタデータをそれぞれのノイズイベントのシグネチャと比較することを含み、ここでシグネチャは、それぞれのノイズイベントにさらされたマイクロフォンデバイスの複数の周波数応答に適用される主成分分析を使用して生成される、ことを特徴とする方法。
- 請求項3に記載の方法であって、前記複数の周波数応答が、
制御された条件でキャプチャされたサンプル、および
ユーザの入力から得られたサンプル
の少なくとも1つを含む、ことを特徴とする方法。 - 請求項2~4のいずれかに記載の方法であって、前記メタデータは、周波数応答スペクトルを含む一方、前記メタデータを参照メタデータと比較することは、NMDの集団から収集された周波数応答スペクトルに対応する固有空間に周波数応答スペクトルを投影することを含む、ことを特徴とする方法。
- 請求項1~5のいずれかに記載の方法であって、更に、多くのサンプリングフレーム間で平均化された周波数領域の情報にメタデータを限定することによって、メタデータをキャプチャされたサウンドデータから導出するステップを含む、ことを特徴とする方法。
- 請求項1~6のいずれかに記載の方法であって、更に、
低いレベルのノイズが検出された場合、サウンドデータを分析するために処理されるマイクロフォンのチャンネル数を減らすステップを含む、ことを特徴とする方法。 - 請求項7に記載の方法であって、マイクロフォンのチャネル数を減らすステップは、1つ以上のマイクロフォンの、チャンネルをオフにする、チャンネルの電源を落とす、又は、チャンネルからの読み取り値を廃棄することである、ことを特徴とする方法。
- 請求項1~8のいずれかに記載の方法であって、NMDの少なくとも1つの性能パラメータを修正するステップは、
前記NMDのウェイクワード検出感度パラメータを調整すること、
前記NMDに関連する再生デバイスの再生音量を調整すること、
前記NMDのノイズキャンセリングアルゴリズムを変更すること、及び
環境に存在する複数のNMDの少なくとも1つの性能パラメータを修正すること、
の少なくとも1つを含む、ことを特徴とする方法。 - 請求項1~9のいずれかに記載の方法であって、更に、
NMDを介して、分類されたノイズに対応するデータを、ワイドエリアネットワークを通って1つ以上のリモートコンピューティングデバイスに送信するステップ
を含む、ことを特徴とする方法。 - 請求項1~10のいずれかに記載の方法であって、前記メタデータは、
マイクロフォンの周波数応答データ、
マイクロフォンのスペクトルデータ、
アコースティックエコーキャンセレーション(AEC)データ、
エコーリターンロスエンハンスメント(ERLE)データ、
アービトレーションデータ、
信号レベルデータ、及び
方向検出データ
の少なくとも1つを含む、ことを特徴とする方法。 - 請求項1~11のいずれかに記載の方法であって、NMDの少なくとも1つの性能パラメータを修正するステップは、
NMDまたはリモートデバイスによって、シミュレートされたノイズイベントまたはノイズイベントと音声入力の組合せに適用される種々の性能パラメータを用いて行われる、予測型モデリングを実行することと、
特定のノイズイベントまたは組み合わせされたノイズイベントについて、最適な性能パラメータ値を決定することと、
決定された最適な性能パラメータ値に基づいて、NMDの性能パラメータを修正すること、
を含む、ことを特徴とする方法。 - 請求項12に記載の方法であって、前記予測型モデリングは、既知のノイズのセットでトレーニングされたニューラルネットワークを用いて実行される、ことを特徴とする方法。
- ネットワークマイクロフォンデバイス(NMD)に、請求項1~13のいずれかに記載の方法を実行させるために、1つ以上のプロセッサによって実行可能な命令を格納した有形、非一時的、コンピュータ可読媒体。
- ネットワークマイクロフォンデバイス(NMD)であって、
1つ以上のプロセッサと、
1つ以上のマイクロフォンと、
請求項14に記載の有形、非一時的、コンピュータ可読媒体と
を含む、ネットワークマイクロフォンデバイス。
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