JP7462400B2 - 問題騒音の発音源を識別するための騒音データの人工知能装置および前処理方法 - Google Patents
問題騒音の発音源を識別するための騒音データの人工知能装置および前処理方法 Download PDFInfo
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Description
S2 単位フレーム選定
S3 N個のセグメントに分割
S4 ログメルフィルタ適用、周波数成分抽出
S5 特徴ベクトル出力
S6 人工知能を用いた問題騒音発音源の識別
Claims (8)
- 騒音データ学習部を介して、時間によってサンプリングした騒音のうち問題騒音に対して単位フレームを選定するステップと、
セグメント分割部を介して、前記単位フレームをN個のセグメントに分割するステップと、
セグメント分析部と、平均処理部を介して、前記セグメント毎に周波数特性を分析し、ログメルフィルタ(Log Mel Filter)を適用して前記周波数特性を抽出するステップと、
学習モデル統合部と、学習モデル生成部と、特徴パラメータ抽出部を介して、前記セグメントに関する情報を平均し、1個の代表フレームとして特徴ベクトル(特徴パラメータ)を出力するステップと、を含む、問題騒音の発音源を識別するための騒音データの前処理方法を含み、
前記前処理方法により前記時間の変化に従って抽出された特徴パラメータによる人工知能学習に、双方向RNN(Bidirectional RNN)を適用することを特徴とする、問題騒音の発音源を識別するための騒音データ人工知能学習方法。 - 前記サンプリングは、問題周波数帯域の2倍の範囲でサンプリングすることを特徴とする、請求項1に記載の問題騒音の発音源を識別するための騒音データ人工知能学習方法。
- 前記時間による単位フレームと、次の時間の単位フレームとの間には、オーバーラップを設定することを特徴とする、請求項1に記載の問題騒音の発音源を識別するための騒音データ人工知能学習方法。
- 前記人工知能学習にDNN(Deep Neural Network)をさらに適用することを特徴とする、請求項1に記載の問題騒音の発音源を識別するための騒音データ人工知能学習方法。
- 前記人工知能学習にアテンションメカニズム(Attention Mechanism)をさらに適用することを特徴とする、請求項4に記載の問題騒音の発音源を識別するための騒音データ人工知能学習方法。
- 前記人工知能学習にアーリーステージアンサンブル(Early stage ensemble)アルゴリズムをさらに適用することを特徴とする、請求項5に記載の問題騒音の発音源を識別するための騒音データ人工知能学習方法。
- 前記問題騒音の学習データの時間軸が一定に収集される場合、
正確度を向上するために、時間‐周波数マップとエンジン回転数‐周波数マップの両方を用いるEnsemble model of jointly trained RNNsアルゴリズムをさらに適用することを特徴とする、請求項6に記載の問題騒音の発音源を識別するための騒音データ人工知能学習方法。 - 請求項7に記載の問題騒音の発音源を識別するための騒音データ人工知能学習方法が実現された装置であって、
前記装置の入力手段で車両またはパワートレインの騒音を直接測定するか、格納されている騒音データを格納媒体を用いて提供することを特徴とする、問題騒音の発音源を識別するための騒音データ人工知能学習装置。
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