JP3546236B2 - Noise psychological evaluation method, apparatus and medium - Google Patents

Description

のようにt_stepずつシフトする。物理ファクターは、ACF及びIACFの各ステップから導出する。2Tは予測されるτ_eの値よりも十分長くする必要がある。また、これは、各ステップに対する知覚の「聴覚の時間窓」に大きく関連する。環境騒音に対する2Tとしては、概ね０．１〜０．５秒が適している。２Ｔがこの範囲よりも小さい場合、(τ_e)_minがある値に収束する。一般的に、t_stepは０．１秒が好適である。変動が細かい場合は、より短いt_stepを選択する。よく知られているように、バイノーラル信号をＦＦＴ（高速フーリエ変換）と、その後逆ＦＦＴの処理を行うことにより、ACF及びIACFを得ることができる。Ａ特性フィルター及び、マイクロフォンの周波数特性は、ＦＦＴ処理の後で考慮する。
【００１８】
左右の耳の部位におけるACFを、それぞれ、Φ_ll(τ)、Φ_rr(τ)で表わす。特定の数字の場合は、Φ_ll ⁽ⁱ⁾、Φ_rr ⁽ⁱ⁾で表わす（1<i<Tf、 f:サンプリング周波数(Hz)、i:整数）。左右のΦ(0)を計算するためには、Φ_ll ⁽ⁱ⁾とΦ_rr ⁽ⁱ⁾を下記のように平均する。
【数２】

SPLの正確な値は、次式で得られる。
【数３】

Φ_ref(0)は、基準音圧値２０μPにおけるΦ(0)である。
【００１９】
バイノーラルの聴取音圧レベルは、Φ_ll(0)及びΦ_rr(0)の相乗平均である。
【数４】

このΦ(0)は、IACFを正規化する際の分母となるものであるため、IACFファクターの一方のもの、或いは右半球の空間ファクターに分類されるものと考える。正規化したＡＣＦの振幅が０．１（１０％の遅延）になる時の遅延時間によって、有効継続時間τ_eを定義する。正規化した左右の耳におけるＡＣＦ、φ_ll,rr（τ）は、次式で得られる。
【数５】

【００２０】
図４は、縦軸にＡＣＦの対数の絶対値、横軸に遅延時間をとったグラフである。
図４に示すように初期のＡＣＦが線形に減少するのが一般的に観察できるため、縦軸をデシベル（対数）に変換するとτ_eを容易に得ることができる。線形回帰の場合は、ある一定の短い時間Δτにおいて得られるAＣＦのピークに対して最小平均自乗法(LMS)を使用する。このΔτは、ACFのピークを検知するために使用され、計算前に慎重に決定しておく必要がある。τ_eを計算する際、原点が回帰線上にない場合、ＡＣＦの原点（ACF=0、τ＝０）を、考慮に入れなくても良い場合も多い。極端な例では、目的とする騒音が純音とホワイトノイズとを含む場合、原点において急激な減衰が観察される。その後の減衰は、純音成分のため一定に保たれる。この場合、ＡＣＦ関数の解は求まらない。
【００２１】
図５は、縦軸に正規化したＡＣＦ、横軸に遅延時間をとったグラフである。
図５に示すように、τ_１は正規化したＡＣＦの第１のピークまでの遅延時間、φ_１はその第１ピークでの振幅である。第１ピークは、局所的な小さなピークは無視して、主要なピークに基づき決定する。ファクターτ_nとφ_n(N≧2)とは考慮に入れない。なぜなら、τ_nとφ_nは、一般的にτ₁とφ_１とに相関関係があるからである。
【００２２】
図６は、縦軸に正規化したＩＡＣＦ、横軸に左右の信号の遅延時間をとったグラフである。左右の耳の音響信号の間のＩＡＣＦは、φ_lr(τ)（-1<τ<+1[ms]）で表わされる。デジタル形式では、Φ_lr ⁽ⁱ⁾（-f/10³≦i≦f/10³、iは整数であり、これが負の場合は左のチャンネルに遅れがあるＩＡＣＦであることを示す）。両耳の間の最大遅延としては−１から＋1msを考慮すれば十分である。最大振幅ＩＡＣＣは主観的拡散に関連するファクターである。図６に示すように、正規化されたＩＡＣＦΦ_lr ⁽ⁱ⁾の最大振幅は遅延範囲内で得られる。即ち
ＩＡＣＣ＝｛φ_lr ⁽ⁱ⁾｝_max （５）
正規化されたＩＡＣＦは次式で得られる。
【数６】

【００２３】
τ_IACCの値は、最大振幅の遅延時間において容易に求まる。例えば、τ_IACCが正の場合、音源は聴者の右側に位置する、或いは音源が右側にあるかのように知覚する。図６に示すように、最大振幅における幅Ｗ_ＩＡＣＣを、最大値から０．１（ＩＡＣＣ）下の部分のピーク幅で得ることができる。この係数０．１はＩＡＣＣ＝１．０におけるJNDとして概算的に用いられるものである。聴取音圧レベルＬＬは、式（２）でSPLをLLと置き換えることによって得られる。このようにして、各物理ファクターを、ＡＣＦ及びＩＡＣＦから求めることができる。
【００２４】
次に、ＡＣＦファクターに基づき騒音源の種類の特定する方法について説明する。
騒音源の種類は、４つのＡＣＦファクター遅れ時間が０で表わされるエネルギー（Φ(0)）、有効継続遅延時間（τ_e）、ＡＣＦの第１ピークまでの遅延時間（τ₁）、正規化したＡＣＦの第１ピークの振幅（φ₁）を用いて特定する。Φ(0)は騒音源と聴者との距離に応じて変化するため、距離が不明の場合は、計算の条件には特別に注意を払う必要がある。たとえファクターΦ(0)が有効でない場合であっても、その他の３つのファクターを用いて騒音源の種類を特定することができる。空間情報が変化する場合、残りのＩＡＣＦファクターを考慮に入れることもできる。音響信号の最も大きく変動する部分である最小τ_e：(τ_e)_minを用いる理由の１つは、この部分が主観的な応答に最も深く関与するものであるということである。
【００２５】
未知の対象データ（下記の式(7)~(10)では記号aで示す）用の(τ_e)_minにおける各ファクターの値とデータベースに格納されたテンプレート用（記号bで示す）の値との差、即ち「距離」を計算する。ここで「対象」とは、システムによって特定されるオブジェクトとしての環境騒音のことを意味する。テンプレート値は、ある特定の環境騒音に対する典型的なＡＣＦファクターのセットであり、これらの複数のテンプレートを未知の騒音と比較する。
距離Ｄ（ｘ）（ｘ：Φ(0)、τ_e、τ₁、φ_１）を次式により計算する。
【数７】

【００２６】
目的とする騒音源の合計距離Ｄは、次式で表わされる。
【数８】

Ｗ^(x)（x;Φ(0)、(τ_e)_min、τ₁、φ₁）は、重み係数である。この算出された距離Ｄに最も近いDを有するテンプレートを、求める騒音源であると判断する。これにより、未知の騒音源が、何であるのか、例えば鉄道、自動車、航空機、工場騒音であるのか、更にその車種、機種などを特定することが可能となる。
【００２７】
図７は重み係数の計算方法を説明するブロック図である。式（１１）の重み係数Ｗ^(x)（x;Φ(0)、τ_e、τ₁、φ₁）は、統計値S₁ ⁽ⁱ⁾とS₂ ⁽ⁱ⁾とを用いて得ることができる。図７に示すように、S₁ ⁽ⁱ⁾は、ＡＣＦファクターの全カテゴリーに対する標準偏差（SD）の算術平均である。ここでカテゴリーとは、同じ種類の騒音に対するデータのセットを意味する。S₂ ⁽ⁱ⁾は、各カテゴリの算術平均の標準偏差である。Ｗ^(x)は、ファクター{（S₂/S₁）^1/2}_maxの中の最大値で正規化した後、（S₂/S₁）^1/2で得られる。この平方根の処理は経験的に得られたものである。騒音源の間におけるより大きなSDと、ある騒音の間におけるより小さなSDとのファクターとは他の種類の騒音とは区別できるため、このようなファクターの重みはその他のファクターのものよりも大きくなる。テンプレートを改善する学習機能がある場合、システム上においてテンプレートは、システム内でＡＣＦの各ファクターについての最新の値と、元の値との平均によって上書きすることもできる。
【００２８】
図８は、聴覚−大脳機能システムのモデルを説明するブロック図である。聴覚−大脳機能システムのモデルは、自己相関（ＡＣＦ）メカニズム、両耳間相互相関（ＩＡＣＦ）メカニズム、左右大脳の機能分化を含んでいる。信号のパワースペクトルに含まれる情報は、音響信号のＡＣＦにも含まれていることは注目すべきことである。また騒音場の空間的感覚を示すため、ＩＡＣＦより抽出される空間的ファクターを考慮する。音色は音の基本的感覚と空間的感覚を含む総合的な感覚として定義される
【００２９】
聴覚−大脳機能モデル（図８）を使って、自由空間内に存在する聴者の正面にある与えられた音響信号ｐ(t)の基本的な感覚を考える。ここで長時間ＡＣＦを次式で得ることができる。
【数９】

ｐ’(t)=p(t)*s(t)で、s(t)は耳の感度である。便宜上s(t)はＡ特性のインパルス応答が用いられる。パワースペクトルも次式のようにＡＣＦから得ることができる。
【数１０】

このように、ＡＣＦとパワースペクトルは数学的には同じ情報を含んでいる。
【００３０】
ＡＣＦの解析において３つの重要な事項として、遅れ時間が０で表わされるエネルギーΦ_p(0)と、正規化したＡＣＦのエンベロープから抽出される有効継続時間τ_eと、ピークやディップやその遅れ時間とを含む微細構造とがある。図４に示すように、この有効継続時間τ_eは、10パーセント遅れ時間として定義でき、騒音響信号それ自身に含まれる繰り返し成分、または残響成分として表わされる。前述したように正規化したＡＣＦはΦ_p(τ)＝Φ_p(τ)／Φ_p(0)で得ることができる。
【００３１】
ラウドネスＳ_Lは次式で表わされる。
Ｓ_L=f_L(Φ(0),τ₁,φ₁,τ_e) （１５）
即ち、ＡＣＦファクターである、遅れ時間が０で表わされるエネルギー（Φ(0)）、有効継続遅延時間（τ_e）、ＡＣＦの第１ピークまでの遅延時間（τ₁）、正規化したＡＣＦの第１ピークの振幅（φ₁）からラウドネスを求めることができる。
ここでτ₁は騒音のピッチまたは後述するミッシングファンダメンタル現象に関係するものである。また、p’(t)が音圧レベルL(t)を与えるための圧力２０μPaを基準として測定されるなら、等価騒音レベルL_eqは次式で求めることができる。
【数１１】

このＬ_eqは１０logΦ_p(0)に相当するものである。また、サンプリング周波数は、最大可聴周波数域の２倍以上としなければならないので、通常の騒音計で測定されたL_eqよりも極めて精度良く測定できる。
【００３２】
図９は、縦軸にラウドネス尺度値、横軸にバンド幅をとったグラフである。このグラフは、Φ_p(0)を一定とした条件下での一対比較テスト（１０８０dB/octaveのスロープを持つフィルタを使用）で得られた臨界帯域内のラウドネス尺度値を示したものである。明らかに純音のような騒音が同じ繰り返し成分を持つとき、τ_eは大きな値となり、ラウドネスが大きくなる。このように、ラウドネス対バンド幅の関係は、臨界帯域内でも平坦にならないことがわかる。なお、この結果は中心周波数１kHzの周波数帯域で得られたものである。
【００３３】
騒音のピッチまたはミッシングファンダメンタルは次式で表わされる。
Ｓ_p=f_p(τ₁,φ₁) （１７）
ここで、ミッシングファンダメンタル現象とは、いくつかの倍音構造が存在するとき、実際にはない高さの音が聞こえるという現象である。
【００３４】
最も複雑な知覚である音色は、次式で表わされる。音色には、ラウドネスやピッチも含まれるものである。
S_T=f_T[Φ(0),τ_e,(τ₁,φ₁),…,(τ_n,φ_n)] （１８）
τ_n,φ_n（n=1,2,…）の中でτ₁,φ₁が最も顕著な直交ファクターであるため式（１８）は以下のように書き直すことができる。
S_T=f_T[Φ(0),τ_e,τ₁,φ₁] （１９）
【００３５】
信号の時間的長さの知覚に関する感覚は、次式で表わされる。
S_D=f_D[Φ(0),τ_e,τ₁,φ₁] （２０）
【００３６】
長時間ＩＡＣＦは次式で求めることができる。
【数１２】

ここでp’_l,r(t)=p(t)_l,r*s(t)、はp(t)_l,r は左右外耳道入り口の音圧である。
【００３７】
騒音源の水平面の方向の知覚を含む空間情報の知覚は次式で表わされる。
Ｓ＝f(LL,IACC,τ_IACC,W_IACC) （２２）
ここで聴取音圧レベルＬＬは{Φ_ll(0),Φ_rr(0)}である。記号{}は、左右の耳の入り口に到来する信号のτ＝０のときのＡＣＦであるΦ_ll(0)、Φ_rr(0)の組を表わす。数学的にはＬＬは、両耳に到来する音響信号のエネルギーの算術平均で次式のように表わされる。
【数１３】

式（２２）で示す４つのＩＡＣＦファクター（直交ファクター）の中で、−１〜＋１msの範囲内のτ_IACCは、水平方向の音源の水平方向の知覚に関する重要なファクターである。正規化したＩＡＣＦが１つの鋭いピークを持ち、ＩＡＣＣが大きく、高周波数成分によってＷ_ＩＡＣＣが小さい値であるとき、明確な方向感が得られる。逆に主観的拡がり感やあいまいな方向感はＩＡＣＣが小さい値（＜０．１５）の時に起こる。
【００３８】
正中面に位置する騒音源の知覚については、耳の入り口に到来する音響信号の長時間ＡＣＦから抽出される時間的ファクターを式（２２）に加えるべきであろう。
図８に示すように、注目すべきはＩＡＣＣに相当する下丘付近に存在する神経活動の存在である。また、室内音場においては、ＬＬとＩＡＣＣとは右大脳半球に支配的に関連があり、時間的ファクターであるΔt₁やＴ_subは左大脳半球と関わっていることを発見した。
【００３９】
主観的拡がり感の尺度値を得るため、２つの対称な反射音の水平入射角度を変更し、ホワイトノイズを用いて一対比較テストを行った。被験者は、ＬＬ、τ_IACC、W_IACCが一定の条件下で、提示された２つの音場のうち、どちらの音場がより広がって聞こえるかを判断した。図１０は、左縦軸に拡がり感の尺度値、右縦軸に最大振幅ＩＡＣＣ、横軸に反射音の水平入射角度をとったグラフである。図１０に示すように、２５０Hz~4kHz（図１０(a):250Hz、(b):500Hz、(c):1kHz、(d):2kHz、(e):4kHz）の周波数帯域の結果において、尺度値と最大振幅ＩＡＣＣとは強い負の相関関係を示した。従って、上述した実験結果により、主観的尺度値を、ＩＡＣＣの３／２乗で次式のように求めることができる。
Ｓ_diffuseness=−α（IACC）^β （２４）
実験により求めた係数αは２．９、乗数βは３／２である。
【００４０】
騒音場の見かけの音源の幅（ＡＳＷ）を求める方法について説明する。低域の周波数成分が大きい騒音場では、長時間ＩＡＣＦは遅れ時間τが−１〜＋１msの範囲内に明確なピークを持たず、W_IACCは大きくなる。このW_IACCは次式で求めることができる。
【数１４】

ここで、Δω_cは２π（f₁+f₂）、f₁とf₂とは、それぞれ理想的なバンドパスフィルターの下限値と上限値である。便宜上、δは０．１（ＩＡＣＣ）と定義する。
【００４１】
注目すべきことは、大きなＡＳＷは低周波数帯域が多く、ＩＡＣＣが小さいときに知覚されるということである。すなわち、ＬＬが一定でτ_IACC＝０の条件下では、ＡＳＷはＩＡＣＣとＷ_IACCのＩＡＣＦファクターに基づき求めることができる。ＡＳＷの尺度値を１０名の被験者を用いて一対比較テストで求めた。Ｗ_IACCの値を制御するため、１／３オクターブバンドパスノイズの中心周波数を２５０Hz〜２kHzで変化させた。ＩＡＣＣは直接音に対する反射音のレベルの比を制御して調整した。聴取音圧レベルＬＬは、ＡＳＷに影響するので、全ての音場の耳の入り口でのトータル音圧レベルはピーク値が７５ｄＢＡで一定とした。被験者は提示された２つの音場のうちどちらかが広がって聞こえるかを判断した。尺度値Ｓ_ASWの分散分析の結果、ＩＡＣＣ、Ｗ_IACCの両方のＩＡＣＦファクター共に有意であり（p<0.01）、以下のようにS_ASWに対して独立に寄与している。従って、S_ASWを次式で求めることができる。
Ｓ_ASW＝ａ(ＩＡＣＣ)^３／２＋ｂ（Ｗ_IACC）^１／２ （２６）
ここで係数ａ＝−１．６４、ｂ＝２．４４であり、これらの係数は、図１１（ａ）（ｂ）に示す１０名の被験者の尺度値の回帰曲線から得られたものである。図１１（ａ）は縦軸にＡＳＷ、横軸にＩＡＣＣをとったグラフであり、図１１（ｂ）は縦軸にＡＳＷ、横軸にＷ_ＩＡＣＣをとったグラフである。また、図１１は、縦軸に実際に測定したＡＳＷの尺度値、横軸に計算されたＡＳＷの尺度値をとったグラフである。図１２に示すように、この式から求めたＳ_ＡＳＷの尺度値と、Ｓ_ＡＳＷの測定値はよく対応することを確かめた（r=0.97、p<0.01）。
【００４２】
時間的に変動する環境騒音を評価するため、短時間ランニングＡＣＦ及び短時間ランニングＩＡＣＦを用いる。前述と同様の方法で抽出された短時間ランニングの空間的・時間的ファクターは、時変動する騒音場の基本的感覚を示すのに用いられている。短時間ＡＣＦは次式で求めることができる。
【数１５】

ここで２Ｔは解析される信号の長さである。この長さ２Ｔは、ランニングＡＣＦの有効継続時間の最小値（τ_e）_minを少なくとも含む範囲で決定すべきである。（τ_e）_minを示す騒音は信号が最も急速に変動することを表わしており、この部分が最も主観的応答に影響を及ぼしている。
【００４３】
各騒音の部分におけるラウドネスＳ_Lに関して、式（１５）は次式のように書き換えることができる。
Ｓ_Ｌ＝ｆ_Ｌ（ＬＬ,τ₁,φ_１,τ_e） （２８）
ここで各ファクターは各騒音の部分について得られ、式（１５）のΦ(0)はＬＬに置き換えられる。ＡＣＦから抽出された時間的ファクターが、室内の反射音群（Δt₁,Δt_２,．．．）と後続残響時間Ｔ_subに影響を及ぼしているはずだということに注目すべきである。
【００４４】
環境騒音場のピッチの記述で、騒音場において有意な時間的ファクターはτ_１とφ_１とであり、従って式（１７）はそのまま保たれる。
【００４５】
環境騒音場の音色は時間的・空間的ファクター全てで、次式のように表わされる。
Ｓ_Ｔ＝ｆ_Ｔ（τ_e,τ₁,φ_１；ＬＬ,ＩＡＣＣ,τ_IACC,Ｗ_IACC） （２９）
ここで、人間の大脳半球が時間的ファクターが左大脳半球に関連し、空間的ファクターが右大脳半球に関連しているという専門化を考えると、式（２９）は以下のように置き換えることができる。
Ｓ_Ｔ＝ｆ_T（τ_e,τ₁,φ_１）_left＋ｆ_T（ＬＬ、ＩＡＣＣ,τ_IACC,Ｗ_IACC）_right（２９）
弱い反射音の閾値をΔt₁の関数として図１３に示す。式（２９）に含まれる、聴者に対する反射音の空間的方向（ＩＡＣＣとτ_IACC）と反射音の遅れ時間Δt1はこの閾値を示している。
【００４６】
耳の感度は外耳と中耳とを含む物理システムにより特徴づけられる。音響信号を解析する前に、便宜上、Ａ特性をかけておく。
単一反射音の遅れ時間を関数とした単音節の明瞭度は、母音と子音との間の部分の短時間ＡＣＦから抽出された４つの直交ファクターを解析することにより予測できる。最近の調査では、音色や比類似度の判断は、コンサートホール内の音場の主観的プリファレンスと同じく、総合的な主観的応答であることを明確に示している。音色と同様に、主観的プリファレンスは、τ_eの最小値を用いて表わされる。短時間積分時間は次式で表わされる。
（２Ｔ）＝３０（τ_e）_min （３０）
精神作業に関する騒音の影響は、作業能率と大脳の専門化との間の妨害現象として解釈することができる。ＡＣＦから抽出された時間的ファクターは、左大脳半球に関連しており、ＩＡＣＦから抽出されるファクターは右大脳半球に主に関わっている。
【図面の簡単な説明】
【図１】本発明による装置の具体的な構成を示す装置概略図である。
【図２】本発明による騒音源の種類の特定、心理評価を行う方法のフローチャートである。
【図３】図３は、ピーク検知処理手順を説明する図であって、縦軸にノイズレベル、横軸に時間をとったグラフであって、その下段に積分間隔を示す図である。
【図４】縦軸にＡＣＦの絶対値の対数、横軸に遅延時間をとったグラフである。
【図５】縦軸に正規化したＡＣＦ、横軸に遅延時間をとったグラフである。
【図６】縦軸に正規化したＩＡＣＦ、横軸に左右の信号の遅延時間をとったグラフである。
【図７】重み係数の計算方法を説明するブロック図である。
【図８】聴覚−大脳機能システムのモデルを説明するブロック図である。
【図９】縦軸にラウドネス尺度値、横軸にバンド幅をとったグラフである。
【図１０】左縦軸に拡がり感の尺度値、右縦軸に最大振幅ＩＡＣＣ、横軸に反射音の水平入射角度をとったグラフである。
【図１１】（ａ）は縦軸にＡＳＷ、横軸にＩＡＣＣをとったグラフであり、（ｂ）は縦軸にＡＳＷ、横軸にＷ_ＩＡＣＣをとったグラフである。
【図１２】縦軸に実際に測定したＡＳＷの尺度値、横軸に計算されたＡＳＷの尺度値をとったグラフである。
【図１３】縦軸に信号の閾値、横軸に遅延時間をとったグラフである。
【符号の説明】
１ 頭部の模型
２ バイノーラル方式の音声採取手段
３ ＬＰＦ（ローパスフィルタ）、
４ Ａ／Ｄコンバータ
５ コンピュータ
６ 音響信号記憶手段
７ ＡＣＦ演算手段
８ ＩＡＣＦ演算手段
９ ＡＣＦファクター演算手段９
１０ ＩＡＣＦファクター演算手段１０
１１ 騒音源種類特定手段
１２ 心理評価手段
１３ データベース[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a method and apparatus for measuring and psychological evaluation of local environmental noise such as aircraft noise and automobile noise. In particular, the present invention relates to a method and apparatus for measuring and psychological evaluation of noise by a binaural method.
[0002]
[Prior art]
2. Description of the Related Art Conventionally, local environmental noises such as aircraft noise and automobile noise have been discussed with respect to sound pressure levels measured using a mono-aural sound level meter and their frequency characteristics. However, it has been found that the physical factors measured by the above-described mono-oral method alone are insufficient and inadequate to express human subjective responses. Also, in concert hall acoustics, the binaural method has revealed the psychological (subjective) relationship with the physical data of the hall, but in the noise field, most are related to the mono-aural method. is there.
[0003]
[Problems to be solved by the invention]
For many years, environmental noise has been evaluated using the statistics of Sound Pressure Level (SPL). This SPL is L_xOr L_eqAnd its power spectrum is measured with a mono-aural sound level meter. However, this SPL and power spectrum alone are not suitable for subjective evaluation of environmental noise.
[0004]
That is, an object of the present invention is to provide a method for specifying the type of a noise source using physical factors derived from an autocorrelation function and a cross-correlation function that change every moment in a time domain based on a human auditory-cerebral function system. , Devices and media. Another object of the present invention is to provide a more accurate loudness, a pitch, and a physical factor derived from an autocorrelation function and a cross-correlation function that change every moment in a time domain based on a human auditory-cerebral function system. An object of the present invention is to provide a method, an apparatus, and a medium for performing psychological evaluation such as a tone, a sense of psychological time, a subjective feeling of spreading, and an apparent sound source width of a noise field.
[0005]
[Means for Solving the Problems]
In order to achieve the above-mentioned object, an audio signal recording step of collecting and recording an acoustic signal of environmental noise using a voice sampling means, and an autocorrelation function (Fourier transform) from the recorded acoustic signal by a calculation means by an arithmetic means. ACF calculation step for calculating (ACF), ACF factor calculation step for calculating each ACF factor by the calculation means from the calculated ACF, and determination of the type of the noise source by the calculation means using the obtained ACF factors And a method for identifying the type of the noise source.
[0006]
Preferably, in the above-mentioned method for specifying the type of a noise source, the step of calculating the ACF factor includes an energy (Φ (0)) in which a delay time, which is an ACF factor, is represented by 0 from the calculated ACF. Effective duration delay time (τ_e), The delay time until the first peak of the ACF (τ₁), The amplitude of the first peak of the normalized ACF (φ₁), And the determining step of determining the type of the noise source includes: an energy (Φ (0)) having a delay time of 0, which is the calculated ACF factor; τ_e), The delay time until the first peak of the ACF (τ₁), The amplitude of the first peak of the normalized ACF (φ₁) To obtain distances which are absolute values of the difference between the logarithm of the logarithm and the logarithm of the corresponding template for each ACF factor of the noise source prepared in advance, and the standard deviation of the arithmetic mean of each of the ACF factors in advance Is S₂Is the arithmetic mean of the standard deviations for all categories of the ACF factor, S₁And a weighting factor that is a square root of the divided value is obtained for each ACF factor. Each obtained distance is multiplied by a weighting factor of the corresponding ACF factor obtained in advance to obtain a total distance. , And a comparing / selecting step of comparing the calculated total distance with the distance of the stored template and selecting one of the closest templates. A method is provided for identifying the type of noise source.
[0007]
In order to achieve another object of the present invention, an acoustic signal recording step of recording an acoustic signal of environmental noise in a binaural manner using a voice sampling unit, and using an arithmetic unit from the acoustic signal recorded in the binaural manner. ACF and IACF calculation steps for calculating an autocorrelation function (ACF) and a cross-correlation function (IACF) between the left and right channels, and calculating each ACF factor from the calculated ACF using the calculation means; And / or an ACF / IACF factor calculation step of calculating each IACF factor from the calculated IACF, and a psychological evaluation step of performing a psychological evaluation by using calculation means based on each of the calculated ACF and / or IACF factors, The present invention provides a method for performing a psychological evaluation on a noise source characterized by including:
BEST MODE FOR CARRYING OUT THE INVENTION
[0008]
As well as basic perceptual data such as loudness, pitch, and timbre, the description of many subjective data such as preferences and diffusivity is based on a human auditory-cerebral system response model to the sound field. Although this response model has been predicted, it is known to be consistent with empirical results. For example, recently, the loudness of frequency band limited noise is affected by the effective duration (τ) in the autocorrelation function (ACF), as well as by SPL._e) Are known to be affected. Further, when the fundamental frequency of the complex sound is lower than about 1200 Hz, the pitch and its intensity are respectively determined by the delay time (τ) until the first peak of the ACF.₁), The amplitude of the first peak of the normalized ACF (φ₁). In particular, the τ determined within a certain time_e(Τ_e)_minThe ACF factor obtained in (1) well represents the difference between the subjective evaluation of the noise source and the noise field.
[0009]
This model is composed of the autocorrelation between acoustic signals in two respective paths and the cross-correlation between these acoustic signals, and also takes into account the processing characteristics of the human cerebral hemisphere. That is, an auto-correlation function (ACF) and a cross-correlation function (IACF) are calculated using acoustic signals entering both ears. Energy (Φ (0)) in which the delay time, which is an orthogonal factor, is represented by 0, the effective continuation delay time (τ_e), The delay time until the first peak of the ACF (τ₁), The amplitude of the first peak of the normalized ACF (φ₁) Is derived from the ACF. Also, the listening sound pressure level (LL), the maximum amplitude (IAC), and the delay time (τ)_IACC), Width at maximum amplitude (W_IACC) Is derived from the IACF.
[0010]
FIG. 1 is an apparatus schematic diagram showing a specific configuration of the apparatus according to the present invention. As shown in FIG. 1, a specific example of the apparatus according to the present invention includes a binaural-type sound sampling means 2 (microphone) for sampling an acoustic signal from a noise source mounted on a model 1 of a listener's head, and an LPF 3 (low-pass). Filter), A / D converter 4 and computer 5. As the head, the head of the human body is most desirable, but this is inconvenient. Therefore, a dummy head simulating the head of the human body can be used. However, this dummy head is expensive, and the head model 1 other than the dummy head (a sphere (diameter: 20 cm) using a material such as foamed styrene) is significantly significant in the ACF and IACF measured in the present invention. Since there is no difference, a styrofoam head model was used. The computer 5 includes an audio signal storage unit 6 for storing the collected audio signals, an ACF operation unit 7 for reading out the stored audio signals (left and right two channels) and calculating an ACF based on these audio signals. IACF calculating means 8 for calculating an IACF based on these acoustic signals, ACF factor calculating means 9 for calculating an ACF factor based on the calculated ACF, and IACF factor calculating means for calculating an IACF factor based on the calculated IACF 10, means for specifying the type of noise source based on the calculated ACF factor 11, means for specifying the type of noise source 11, means for performing a psychological evaluation based on the calculated ACF factor and / or IACF factor 12, Database on data used for type identification and psychological evaluation Comprising a scan 13.
[0011]
The left and right two-channel condenser microphones (with microphone amplifier) attached to both ends of the listener's head model 1 are connected to the sound input / output terminal (A / D converter 4) of the portable personal computer 5 via a low-pass filter. I do. Surrounding noise is taken in from the microphone (acoustic signal sampling means 2). Under the management of a program on a computer, measurement, calculation of each physical factor, identification of the type of noise source, psychological evaluation, and the like are performed. In addition, a database relating to data used for identification of the type of noise source and psychological evaluation is constructed.
[0012]
FIG. 2 is a flowchart of a method for specifying the type of noise source and performing psychological evaluation according to the present invention. As shown in FIG. 2, in step S1, an acoustic signal from a noise source is collected by the sound source collecting unit 2. The collected audio signal is converted into a digital signal by the A / D converter 4 via the LPF 3. In step S2, the sound signal collected in step S1 is stored in the sound signal storage means. In step S3, the sound signal stored in step S2 is read. In step S4, ACF and IACF are calculated by the ACF calculation means 7 and the IACF calculation means 8 based on the acoustic signal read in step S3. In step S5, the ACF factor and IACF factor are calculated by the ACF factor calculating means 9 and the IACF calculating means 10 based on the ACF and IACF calculated in step S4. In step S6, based on the ACF factor and IACF factor calculated in step S5, the noise source type specifying unit 11 and the psychological evaluation unit 12 specify the type of the noise source and perform psychological evaluation. At the time of identification and evaluation, data is read from the database 13 storing the template and compared and examined.
[0013]
First, a plurality of measurement sessions are extracted from the collected acoustic signal by a peak detection process. In order to automatically extract environmental noise and target noise from continuous noise, mono-oral energy Φ, which is the energy at the entrance of the left and right ears_ll(0), Φ_rr(0) is analyzed continuously. FIG. 3 is a diagram for explaining the procedure of the peak detection processing, in which the vertical axis represents the noise level, the horizontal axis represents the time, and the lower part thereof represents the integration interval. When the noise is continuous noise such as aircraft noise or train noise, the interval for calculating Φ (0) can be set to be considerably long (for example, 1 second), but the noise is short or intermittent. If so, shorter intervals need to be used. However, in the case of performing the continuous calculation using the expression (1) described later, it is necessary to select an interval longer than the integration interval. Therefore, this interval needs to be determined according to the type of the noise source.
[0014]
As a result, Φ (0) can be determined more accurately than when Φ (0) is determined using an ordinary sound level meter at long time intervals. To detect a peak, the trigger level L must be set in advance._trigMust be set appropriately. Suitable L_trigThe value changes according to the type of target noise, the distance between the target noise and the observer, the atmospheric conditions, and the like. Therefore, this value needs to be determined by preliminary measurement. If the distance between the target noise and the observer is short and there is no interfering noise source near the observer, L_trigIt is easy to determine the value.
[0015]
The noise centered on the maximum value Φ (0) is recorded in a single session using the system. The duration of one session for each desired noise, ie t_sIs L_trigSelect to include the peak of Φ (0) after exceeding the value. For normal environmental noise such as aircraft noise and train noise, t_sThe value is about 10 seconds. This is different for long duration steady state noise and short duration intermittent noise. Note that this system cannot be used in the presence of interfering noise. As shown in FIG. 3, a series of sessions (S₁(t), S_Two(t), S_Three(t),… S_N(t), N: number of sessions, 0 <t <t_s) Is automatically stored on the system.
[0016]
As shown in FIG._sEach session S at_NAnalyze the running ACF and running IACF for (t). Here, only a single session will be considered to explain the "running" process. Before the calculation, a suitable integration interval 2T and a continuous step t_stepDetermine the value of. As mentioned above, the recommended integration interval is about 30 × (τ_e)_min[ms] and this (τ_e)_minIs a series of values τ_e, Which can be easily found by preliminary measurement. This is found using data on different types of environmental noise. In most cases, adjacent integration intervals overlap each other.
[0017]
ACF and IACF are calculated for each step (n = 1, 2,..., M) for each session in the range of 2T. Each step is
(Equation 1)

Like t_stepShift by one. Physical factors are derived from each step of ACF and IACF. 2T is the expected τ_eMust be much longer than the value of. This is also largely related to the perceptual "auditory time window" for each step. As the 2T for the environmental noise, 0.1 to 0.5 seconds is generally suitable. If 2T is smaller than this range, (τ_e)_minConverges to a value. In general, t_stepIs preferably 0.1 second. For small fluctuations, a shorter t_stepSelect As is well known, ACF and IACF can be obtained by performing an FFT (Fast Fourier Transform) and then an inverse FFT on a binaural signal. The A characteristic filter and the frequency characteristics of the microphone are considered after the FFT processing.
[0018]
The ACF at the left and right ear parts is_ll(τ), Φ_rrExpressed by (τ). For specific numbers, Φ_ll ⁽ⁱ⁾, Φ_rr ⁽ⁱ⁾(1 <i <Tf, f: sampling frequency (Hz), i: integer). To calculate left and right Φ (0), Φ_ll ⁽ⁱ⁾And Φ_rr ⁽ⁱ⁾Are averaged as follows:
(Equation 2)

The exact value of SPL is given by:
(Equation 3)

Φ_ref(0) is Φ (0) at the reference sound pressure value of 20 μP.
[0019]
The binaural listening sound pressure level is Φ_ll(0) and Φ_rrThe geometric mean of (0).
(Equation 4)

Since Φ (0) is a denominator when normalizing the IACF, it is considered that the Φ (0) is classified into one of the IACF factors or the space factor of the right hemisphere. The effective duration τ is determined by the delay time when the amplitude of the normalized ACF becomes 0.1 (delay of 10%)._eIs defined. ACF, φ at the normalized left and right ears_{ll, rr}(Τ) is obtained by the following equation.
(Equation 5)

[0020]
FIG. 4 is a graph in which the vertical axis represents the absolute value of the logarithm of the ACF and the horizontal axis represents the delay time.
Since it is generally observed that the initial ACF decreases linearly as shown in FIG. 4, when the vertical axis is converted to decibel (logarithmic), τ_eCan be easily obtained. In the case of linear regression, the least mean square method (LMS) is used for the ACF peak obtained at a certain short time Δτ. This Δτ is used to detect the ACF peak, and needs to be carefully determined before calculation. τ_eIn the calculation of, if the origin is not on the regression line, the origin of the ACF (ACF = 0, τ = 0) often does not need to be taken into account. In an extreme example, when the target noise includes a pure sound and white noise, a sharp attenuation is observed at the origin. Subsequent decay is kept constant for pure tone components. In this case, no solution of the ACF function is obtained.
[0021]
FIG. 5 is a graph with the normalized ACF on the vertical axis and the delay time on the horizontal axis.
As shown in FIG.₁Is the delay time to the first peak of the normalized ACF, φ₁Is the amplitude at the first peak. The first peak is determined based on the main peak, ignoring local small peaks. Factor τ_nAnd φ_n(N ≧ 2) is not taken into account. Because τ_nAnd φ_nIs generally τ₁And φ₁This is because there is a correlation with
[0022]
FIG. 6 is a graph in which the ordinate represents the normalized IACF and the abscissa represents the delay time of the left and right signals. The IACF between the acoustic signals of the left and right ears is φ_lr(τ) (-1 <τ <+1 [ms]). In digital form, Φ_lr ⁽ⁱ⁾(-F / 10^Three≤i≤f / 10^Three, I is an integer, and a negative value indicates an IACF with a delay in the left channel). It is sufficient to consider the maximum delay between both ears from -1 to +1 ms. The maximum amplitude IACC is a factor related to subjective spreading. As shown in FIG. 6, the normalized IACFΦ_lr ⁽ⁱ⁾Are obtained within the delay range. That is
IACC = ｛φ_lr ⁽ⁱ⁾｝_max         (5)
The normalized IACF is obtained by the following equation.
(Equation 6)

[0023]
τ_IACCIs easily obtained at the maximum amplitude delay time. For example, τ_IACCIs positive, the sound source is located on the right side of the listener, or the sound source is perceived as if it were on the right side. As shown in FIG. 6, the width W at the maximum amplitude_IACCCan be obtained with a peak width of 0.1 (IACC) below the maximum value. This coefficient of 0.1 is roughly used as JND at IACC = 1.0. The listening sound pressure level LL is obtained by replacing SPL with LL in equation (2). In this way, each physical factor can be obtained from the ACF and the IACF.
[0024]
Next, a method of specifying the type of the noise source based on the ACF factor will be described.
The type of noise source is energy (Φ (0)) in which four ACF factor delay times are represented by 0, and effective continuation delay time (τ)._e), The delay time until the first peak of the ACF (τ₁), The amplitude of the first peak of the normalized ACF (φ₁). Since Φ (0) changes according to the distance between the noise source and the listener, special attention must be paid to the calculation conditions when the distance is unknown. Even if the factor Φ (0) is not valid, the type of the noise source can be specified using the other three factors. If the spatial information changes, the remaining IACF factors can also be taken into account. The minimum τ which is the largest part of the acoustic signal_e: (Τ_e)_minOne reason for using is that this part is most closely involved in the subjective response.
[0025]
(Τ) for unknown target data (indicated by symbol a in equations (7) to (10) below)_e)_minAnd the difference between the value of each factor and the value for the template (indicated by the symbol b) stored in the database, that is, the “distance” is calculated. Here, “target” means environmental noise as an object specified by the system. The template value is a set of typical ACF factors for a particular environmental noise, comparing these multiple templates with the unknown noise.
Distance D (x) (x: Φ (0), τ_e, Τ₁, Φ₁) Is calculated by the following equation.
(Equation 7)

[0026]
The total distance D of the target noise source is expressed by the following equation.
(Equation 8)

W^(x)(X; Φ (0), (τ_e)_min, Τ₁, Φ₁) Is a weight coefficient. The template having D closest to the calculated distance D is determined to be the noise source to be obtained. As a result, it is possible to specify what the unknown noise source is, for example, a railway, automobile, aircraft, or factory noise, as well as its model and model.
[0027]
FIG. 7 is a block diagram illustrating a method of calculating a weight coefficient. Weighting coefficient W in equation (11)^(x)(X; Φ (0), τ_e, Τ₁, Φ₁) Is the statistic S₁ ⁽ⁱ⁾And S_Two ⁽ⁱ⁾And can be obtained by using As shown in FIG.₁ ⁽ⁱ⁾Is the arithmetic mean of the standard deviations (SD) for all categories of ACF factor. Here, the category means a set of data for the same type of noise. S_Two ⁽ⁱ⁾Is the standard deviation of the arithmetic mean of each category. W^(x)Is the factor {(S_Two/ S₁)^1/2}_maxAfter normalizing with the maximum value in, (S_Two/ S₁)^1/2Is obtained. The processing of this square root has been obtained empirically. The weight of such factors is greater than that of other factors, because the factor of a larger SD between noise sources and a smaller SD between certain noises can be distinguished from other types of noise. . If there is a learning function to improve the template, the template on the system can also be overwritten by the average of the latest value for each factor of the ACF and the original value in the system.
[0028]
FIG. 8 is a block diagram illustrating a model of the auditory-cerebral function system. Models of the auditory-cerebral function system include an autocorrelation (ACF) mechanism, a binaural cross-correlation (IACF) mechanism, and functional differentiation of the left and right cerebrum. It should be noted that the information contained in the power spectrum of the signal is also contained in the ACF of the audio signal. In order to indicate the spatial sensation of the noise field, a spatial factor extracted from the IACF is considered. Tone is defined as an overall sensation that includes the fundamental and spatial senses of sound
[0029]
Using the auditory-cerebral function model (FIG. 8), consider the basic sensation of a given acoustic signal p (t) in front of a listener in free space. Here, the long-time ACF can be obtained by the following equation.
(Equation 9)

p '(t) = p (t) * s (t), where s (t) is ear sensitivity. For convenience, s (t) uses an impulse response of the A characteristic. The power spectrum can also be obtained from the ACF as in the following equation.
(Equation 10)

Thus, the ACF and the power spectrum mathematically contain the same information.
[0030]
In the analysis of the ACF, three important matters are the energy Φ with a delay time of 0._p(0) and the effective duration τ extracted from the normalized ACF envelope_eAnd a fine structure including a peak, a dip, and a delay time thereof. As shown in FIG. 4, this effective duration τ_eCan be defined as a 10% delay time, and is represented as a repetitive component or a reverberant component included in the noise signal itself. The ACF normalized as described above is Φ_p(τ) = Φ_p(τ) / Φ_p(0).
[0031]
Loudness S_LIs represented by the following equation.
S_L= f_L(Φ (0), τ₁, φ₁, τ_e) (15)
That is, the energy (Φ (0)), which is the delay time represented by 0, which is the ACF factor, and the effective continuation delay time (τ_e), The delay time until the first peak of the ACF (τ₁), The amplitude of the first peak of the normalized ACF (φ₁) Can be used to determine loudness.
Where τ₁Is related to the noise pitch or the missing fundamental phenomenon described later. Also, if p ′ (t) is measured based on a pressure of 20 μPa for providing the sound pressure level L (t), the equivalent noise level L_eqCan be obtained by the following equation.
(Equation 11)

This L_eqIs 10logΦ_pIt is equivalent to (0). Since the sampling frequency must be at least twice the maximum audible frequency range, L_eqMeasurement can be performed with much higher accuracy than the above.
[0032]
FIG. 9 is a graph with the loudness scale value on the vertical axis and the bandwidth on the horizontal axis. This graph is Φ_pFIG. 9 shows loudness scale values within a critical band obtained by a pairwise comparison test (using a filter having a slope of 1080 dB / octave) under the condition that (0) is constant. Obviously, when noise like pure tone has the same repetitive component, τ_eHas a large value, and the loudness increases. Thus, it can be seen that the relationship between loudness and bandwidth is not flat even within the critical band. Note that this result was obtained in a frequency band with a center frequency of 1 kHz.
[0033]
The pitch or missing fundamental of noise is expressed by the following equation.
S_p= f_p(τ₁, φ₁) (17)
Here, the missing fundamental phenomenon is a phenomenon in which, when some overtone structures are present, a sound having a height which is not actually present is heard.
[0034]
The tone that is the most complex perception is expressed by the following equation. Tones include loudness and pitch.
S_T= f_T[Φ (0), τ_e, (τ₁, φ₁),…, (Τ_n, φ_n)] (18)
τ_n, φ_nΤ in (n = 1,2,…)₁, φ₁Is the most prominent orthogonal factor, equation (18) can be rewritten as:
S_T= f_T[Φ (0), τ_e, τ₁, φ₁] (19)
[0035]
The sensation related to the perception of the temporal length of the signal is expressed by the following equation.
S_D= f_D[Φ (0), τ_e, τ₁, φ₁] (20)
[0036]
The long-time IACF can be obtained by the following equation.
(Equation 12)

Where p '_{l, r}(t) = p (t)_{l, r}* s (t) is p (t)_{l, r} Is the sound pressure at the entrance of the left and right ear canal.
[0037]
The perception of spatial information including the perception of the direction of the horizontal plane of the noise source is represented by the following equation.
S = f (LL, IACC, τ_IACC, W_IACC) (22)
Here, the listening sound pressure level LL is {Φ_ll(0), Φ_rr(0)}. The symbol {} is the ACF of the signal arriving at the entrance of the left and right ears when τ = 0, Φ_ll(0), Φ_rrRepresents the set (0). Mathematically, LL is the arithmetic mean of the energy of the acoustic signal arriving at both ears and is expressed as:
(Equation 13)

Of the four IACF factors (orthogonal factors) shown in equation (22), τ within the range of −1 to +1 ms_IACCIs an important factor for the horizontal perception of a horizontal sound source. The normalized IACF has one sharp peak, the IACC is large, and W_IACCIs small, a clear sense of direction is obtained. Conversely, a sense of subjective spread and an ambiguous sense of direction occur when IACC is a small value (<0.15).
[0038]
For the perception of a noise source located in the median plane, the time factor extracted from the long-term ACF of the acoustic signal arriving at the ear entrance should be added to equation (22).
As shown in FIG. 8, what should be noted is the presence of neural activity near the inferior colliculus corresponding to IACC. In a room sound field, LL and IACC are predominantly related to the right cerebral hemisphere, and a time factor Δt₁And T_subFound that he was involved in the left cerebral hemisphere.
[0039]
In order to obtain a scale value of the subjective spread, a pairwise comparison test was performed using white noise while changing the horizontal incident angle of two symmetric reflected sounds. Subjects are LL, τ_IACC, W_IACCUnder certain conditions, it was determined which of the two presented sound fields could be heard more widely. FIG. 10 is a graph in which the left vertical axis represents the scale value of the feeling of spreading, the right vertical axis represents the maximum amplitude IACC, and the horizontal axis represents the horizontal incident angle of the reflected sound. As shown in FIG. 10, in the result of the frequency band of 250 Hz to 4 kHz (FIG. 10 (a): 250 Hz, (b): 500 Hz, (c): 1 kHz, (d): 2 kHz, (e): 4 kHz) The scale value and the maximum amplitude IACC showed a strong negative correlation. Therefore, based on the above-described experimental results, the subjective scale value can be obtained by the following equation using the 3/2 power of IACC.
S_diffuseness= −α (IACC)^β             (24)
The coefficient α obtained by the experiment is 2.9, and the multiplier β is 3/2.
[0040]
A method of obtaining the apparent sound source width (ASW) of the noise field will be described. In a noise field having a large low-frequency component, the long-time IACF does not have a clear peak in the range of the delay time τ in the range of −1 to +1 ms._IACCBecomes larger. This W_IACCCan be obtained by the following equation.
[Equation 14]

Where Δω_cIs 2π (f₁+ f_Two), F₁And f_TwoAre the lower limit and upper limit of the ideal bandpass filter, respectively. For convenience, δ is defined as 0.1 (IACC).
[0041]
It should be noted that a large ASW is perceived when the low frequency band is high and the IACC is low. That is, if LL is constant and τ_IACC= 0, ASW is equal to IACC and W_IACCCan be determined based on the IACF factor. ASW scale values were determined in a pairwise comparison test using 10 subjects. W_IACCWas controlled, the center frequency of the 1/3 octave bandpass noise was changed from 250 Hz to 2 kHz. The IACC controlled and adjusted the ratio of the reflected sound level to the direct sound. Since the listening sound pressure level LL affects the ASW, the total sound pressure level at the entrances of the ears in all sound fields was set to a constant peak value of 75 dBA. The subject determined which of the two sound fields presented was widespread and audible. Scale value S_ASWAnalysis of variance, IACC, W_IACCAre significant (p <0.01) for both IACF factors, and S_ASWIndependent contribution to Therefore, S_ASWCan be obtained by the following equation.
S_ASW= A (IACC)^3/2+ B (W_IACC)^1/2             (26)
Here, the coefficients a = -1.64 and b = 2.44, and these coefficients are obtained from the regression curves of the scale values of the ten subjects shown in FIGS. 11 (a) and 11 (b). . FIG. 11A is a graph with ASW on the vertical axis and IACC on the horizontal axis, and FIG. 11B is ASW on the vertical axis and W on the horizontal axis._IACCIt is a graph that takes FIG. 11 is a graph in which the vertical axis indicates the scale value of the actually measured ASW, and the horizontal axis indicates the calculated scale value of the ASW. As shown in FIG. 12, S_ASWScale value and S_ASWIt was confirmed that the measured values corresponded well (r = 0.97, p <0.01).
[0042]
In order to evaluate temporally fluctuating environmental noise, a short-time running ACF and a short-time running IACF are used. The spatial and temporal factors of short running extracted in the same manner as described above are used to indicate the basic sensation of a time-varying noise field. The short-time ACF can be obtained by the following equation.
(Equation 15)

Here, 2T is the length of the signal to be analyzed. This length 2T is the minimum value (τ) of the effective duration of the running ACF._e)_minShould be determined in a range including at least. (Τ_e)_minIndicates that the signal fluctuates most rapidly, and this part has the most influence on the subjective response.
[0043]
Loudness S at each noise part_LEquation (15) can be rewritten as:
S_L= F_L(LL, τ₁, φ₁, τ_e) (28)
Here, each factor is obtained for each noise portion, and Φ (0) in Expression (15) is replaced by LL. The temporal factor extracted from the ACF is the reflected sound group (Δt₁, Δt₂,. . . ) And subsequent reverberation time T_subIt should be noted that
[0044]
In the description of the pitch of the environmental noise field, the significant temporal factor in the noise field is τ₁And φ₁Therefore, equation (17) is kept as it is.
[0045]
The timbre of the environmental noise field is expressed by the following equation with all temporal and spatial factors.
S_T= F_T(Τ_e, τ₁, φ₁LL, IACC, τ_IACC, W_IACC) (29)
Here, considering the specialization that the human cerebral hemisphere has a temporal factor related to the left cerebral hemisphere and a spatial factor related to the right cerebral hemisphere, equation (29) can be replaced as follows: it can.
S_T= F_T(Τ_e, τ₁, φ₁)_left+ F_T(LL, IACC, τ_IACC, W_IACC)_right(29)
Δt is the threshold value for weak reflected sound₁FIG. The spatial directions (IACC and τ) of the reflected sound for the listener included in equation (29)_IACC) And the delay time Δt1 of the reflected sound indicate this threshold value.
[0046]
Ear sensitivity is characterized by a physical system that includes the outer and middle ears. Before analyzing the acoustic signal, the A characteristic is applied for convenience.
The clarity of a single syllable as a function of the delay time of a single reflected sound can be predicted by analyzing four orthogonal factors extracted from the short-time ACF of the portion between the vowel and the consonant. Recent research has clearly shown that the judgment of timbre and relative similarity is an overall subjective response, as is the subjective preference of the sound field in a concert hall. Like the timbre, the subjective preference is τ_eUsing the minimum value of The short integration time is represented by the following equation.
(2T) = 30 (τ_e)_min             (30)
The effect of noise on mental work can be interpreted as a disturbing phenomenon between work efficiency and cerebral specialization. Temporal factors extracted from the ACF are related to the left cerebral hemisphere, and factors extracted from the IACF are mainly related to the right cerebral hemisphere.
[Brief description of the drawings]
FIG. 1 is an apparatus schematic diagram showing a specific configuration of an apparatus according to the present invention.
FIG. 2 is a flowchart of a method for specifying a type of noise source and performing psychological evaluation according to the present invention.
FIG. 3 is a diagram illustrating a procedure of a peak detection process, in which a vertical axis represents a noise level, a horizontal axis represents time, and a lower part thereof represents an integration interval.
FIG. 4 is a graph in which the vertical axis represents the logarithm of the absolute value of the ACF and the horizontal axis represents the delay time.
FIG. 5 is a graph with normalized ACF on the vertical axis and delay time on the horizontal axis.
FIG. 6 is a graph in which the vertical axis represents the normalized IACF and the horizontal axis represents the delay time of the left and right signals.
FIG. 7 is a block diagram illustrating a method of calculating a weight coefficient.
FIG. 8 is a block diagram illustrating a model of the auditory-cerebral function system.
FIG. 9 is a graph with the loudness scale value on the vertical axis and the bandwidth on the horizontal axis.
FIG. 10 is a graph in which a left vertical axis represents a scale value of a feeling of spreading, a right vertical axis represents a maximum amplitude IACC, and a horizontal axis represents a horizontal incident angle of a reflected sound.
11A is a graph with ASW on the vertical axis and IACC on the horizontal axis, and FIG. 11B is a graph with ASW on the vertical axis and W on the horizontal axis._IACCIt is a graph that takes
FIG. 12 is a graph in which the vertical axis represents the actually measured ASW scale value, and the horizontal axis represents the calculated ASW scale value.
FIG. 13 is a graph in which a vertical axis indicates a signal threshold and a horizontal axis indicates a delay time.
[Explanation of symbols]
1 Model of the head
2 Binaural sound sampling means
3 LPF (low pass filter),
4 A / D converter
5 Computer
6 Acoustic signal storage means
7 ACF calculation means
8 IACF calculation means
9 ACF factor calculation means 9
10 IACF factor calculation means 10
11 Noise source type identification means
12 psychological evaluation means
13 Database

Claims (9)

An acoustic signal recording step of recording the acoustic signal of the environmental noise in a binaural manner using a voice sampling means,
ACF and IACF calculation steps of calculating an autocorrelation function ACF and a cross-correlation function IACF between each of the left and right channels from the acoustic signal recorded in the binaural method by using calculation means;
An ACF / IACF factor calculating step of calculating an ACF factor from the calculated ACF using the calculating means and calculating an IACF factor from the calculated IACF;
A psychological evaluation step of performing a psychological evaluation based on the calculated ACF factor and the IACF factor using the arithmetic means;
A noise psychological evaluation method comprising: In the noise psychological evaluation method according to claim 1,
Performing the psychological evaluation,
Based on the calculated IACF factor, the maximum amplitude IACC and the coefficient α, the subjective spread feeling S _diffuseness is _calculated as _follows :
S _diffuseness = -α (IACC) ^3/2
Calculation step obtained by
A noise psychological evaluation method comprising: In the noise psychological evaluation method according to claim 1,
Performing the psychological evaluation,
Based on the calculated IACF factor, the maximum amplitude IACC, the width W _{IACC at the} maximum amplitude, and the coefficients a and b, the apparent sound source width S _ASW is calculated as follows:
^{_{S ASW = -a (IACC) β}} + b (W IACC) 1/2
Calculation step obtained by
A noise psychological evaluation method comprising: Acoustic signal recording means for recording an acoustic signal of environmental noise in a binaural manner using a voice sampling means,
ACF and IACF calculating means for calculating an autocorrelation function ACF and a cross-correlation function IACF between each of the left and right channels from the acoustic signal recorded in the binaural method by using calculating means;
ACF / IACF factor calculating means for calculating an ACF factor from the calculated ACF using the calculating means and calculating an IACF factor from the calculated IACF;
A psychological evaluation means for performing a psychological evaluation based on the calculated ACF factor and the IACF factor using the arithmetic means;
A noise psychological evaluation device characterized by including: The noise psychological evaluation device according to claim 4,
The means for performing the psychological evaluation,
Based on the calculated IACF factor, the maximum amplitude IACC and the coefficient α, the subjective spread feeling S _diffuseness is _calculated as _follows :
S _diffuseness = -α (IACC) ^3/2
Calculation means obtained by
A noise psychological evaluation device characterized by including: The noise psychological evaluation device according to claim 4,
The means for performing the psychological evaluation,
Based on the calculated IACF factor, the maximum amplitude IACC, the width W _{IACC at the} maximum amplitude, and the coefficients a and b, the apparent sound source width S _ASW is calculated as follows:
^{_{S ASW = -a (IACC) β}} + b (W IACC) 1/2
Calculation means obtained by
A noise psychological evaluation device characterized by including: An acoustic signal recording step of recording the acoustic signal of the environmental noise in a binaural manner using a voice sampling means,
ACF and IACF calculation steps of calculating an autocorrelation function ACF and a cross-correlation function IACF between each of the left and right channels from the acoustic signal recorded in the binaural method by using calculation means;
An ACF / IACF factor calculation step of calculating an ACF factor from the calculated ACF using the calculation means and calculating an IACF factor from the calculated IACF;
A psychological evaluation step of performing a psychological evaluation based on the ACF factor calculated using the arithmetic means and the IACF factor.
A computer-readable medium on which a program for evaluating noise psychology is recorded. A computer-readable medium storing the program according to claim 7,
Performing the psychological evaluation,
Based on the calculated IACF factor, the maximum amplitude IACC and the coefficient α, the subjective spread feeling S _diffuseness is _calculated as _follows :
S _diffuseness = -α (IACC) ^3/2
A calculation step of:
A computer-readable medium on which a program for evaluating noise psychology is recorded. A computer-readable medium storing the program according to claim 7,
Performing the psychological evaluation,
Based on the calculated IACF factor, the maximum amplitude IACC, the width W _{IACC at the} maximum amplitude, and the coefficients a and b, the apparent sound source width S _ASW is calculated as follows:
^{_{S ASW = -a (IACC) β}} + b (W IACC) 1/2
A calculation step of:
A computer-readable medium on which a program for evaluating noise psychology is recorded.

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