JPH0336346B2 - - Google Patents
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- Publication number
- JPH0336346B2 JPH0336346B2 JP14196783A JP14196783A JPH0336346B2 JP H0336346 B2 JPH0336346 B2 JP H0336346B2 JP 14196783 A JP14196783 A JP 14196783A JP 14196783 A JP14196783 A JP 14196783A JP H0336346 B2 JPH0336346 B2 JP H0336346B2
- Authority
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- Prior art keywords
- waveforms
- noise
- average value
- signal
- waveform signal
- Prior art date
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- 238000000034 method Methods 0.000 claims description 31
- 230000000737 periodic effect Effects 0.000 claims description 23
- 238000005311 autocorrelation function Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 6
- 238000003672 processing method Methods 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 description 10
- 238000012935 Averaging Methods 0.000 description 5
- JOJYUFGTMHSFEE-YONYXQDTSA-M Cytarabine ocfosphate Chemical compound [Na+].O[C@H]1[C@H](O)[C@@H](COP([O-])(=O)OCCCCCCCCCCCCCCCCCC)O[C@H]1N1C(=O)N=C(N)C=C1 JOJYUFGTMHSFEE-YONYXQDTSA-M 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000005236 sound signal Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Noise Elimination (AREA)
Description
【発明の詳細な説明】
本発明は、音声波形に重なつた雑音のレベルを
低減する方式に関するものである。DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a method for reducing the level of noise superimposed on a speech waveform.
工場、工事現場などの騒音や、列車、航空機、
自動車などの交通騒音の激しい環境で話された音
声は、騒音のために聞き取り難く、円滑な会話を
行うことは困難である。 Noise from factories, construction sites, trains, aircraft, etc.
Speech spoken in an environment with heavy traffic noise, such as a car, is difficult to hear due to the noise, making it difficult to have a smooth conversation.
一方、無線通信による音声の伝送では、伝送途
中における障害のためにＳ／Ｎが低下し、通話が
できなくなることがある。 On the other hand, when audio is transmitted by wireless communication, the S/N ratio may drop due to a failure during the transmission, making it impossible to make a call.
騒音環境でＳ／Ｎのよい音声を得るため、従来
は主として接話マイク、咽喉マイクなどのように
マイクロホンやピツクアツプ方式の開発に頼つて
きた。しかし、これらは騒音レベルの高いときに
効果が不充分であつたり、原理的により品質の音
声が得られないなどの問題がある。 In order to obtain sound with a good S/N ratio in a noisy environment, we have traditionally relied on the development of microphones and pick-up methods, such as close-talk microphones and throat microphones. However, these methods have problems such as being insufficiently effective when the noise level is high, or being unable to obtain higher quality audio in principle.
既に雑音が重畳したり、ひずみの多い音声の
Ｓ／Ｎを改善して会話に対する疲労度を減らし、
了解性を高めるためには、信号源が音声であるこ
とを利用した信号処理が必要であり、これらに関
しては「鈴木誠史、“Ｓ／Ｎの低い音声信号から
雑音を減らす最近の信号処理技術”，日経エレク
トロニクス，No.281，昭和57年１月４日」で紹介
されている。しかし、紹介されている多くの手法
も、動作、性能、価格などに一長一短があるた
め、実用化されているものはない。 Improves the S/N ratio of voices that are already overlaid with noise or has a lot of distortion, reducing fatigue during conversation.
In order to improve intelligibility, signal processing that takes advantage of the fact that the signal source is voice is necessary, and regarding this, Masashi Suzuki, ``Recent signal processing technology to reduce noise from low S/N voice signals'' , Nikkei Electronics, No. 281, January 4, 1981. However, many of the methods that have been introduced have advantages and disadvantages in terms of operation, performance, price, etc., so none have been put into practical use.
これらの中で、入力音声信号を短時間自己相関
関数に変換する音声処理方式SPAC（Speech
Processing system by use of Auto−Corre−
lation function，特許第1045102号，“音声処理
方式”，昭和56年５月28日；鈴木誠史，“自己相関
関数を利用した音声処理方式SPAC”，電子通信
学会，技術研究報告EA75−25，昭和50年７月）
は、評価実験も行われ、ハードウエアも試作され
ていて有望な方法である。しかし、SPACは、ラ
ンダム性雑音の低減能力は大きいが、周期波形を
強調する性質をもつているため、周期性雑音に対
しては効果がない。 Among these, the speech processing method SPAC (Speech
Processing system by use of Auto−Corre−
lation function, Patent No. 1045102, “Speech processing method”, May 28, 1981; Masashi Suzuki, “Speech processing method SPAC using autocorrelation function”, Institute of Electronics and Communication Engineers, Technical research report EA75-25, Showa July 1950)
This is a promising method, as evaluation experiments have been conducted and hardware has been prototyped. However, although SPAC has a great ability to reduce random noise, it has a property of emphasizing periodic waveforms, so it is not effective against periodic noise.
本発明は、音声波形の１周期ごとに短時間自己
相関関数を求め、その相関関数の１周期の波形を
接続して音声波形を合成する過程において、短時
間自己相関関数を平均した波形を差し引くことが
特徴であつて、その目的は雑音が加わつた音声波
形信号からランダム性雑音とともに周期性雑音の
レベルを低減することである。 The present invention calculates a short-time autocorrelation function for each period of a speech waveform, and in the process of connecting the waveforms of one period of the correlation function to synthesize a speech waveform, subtracts the waveform obtained by averaging the short-time autocorrelation functions. Its purpose is to reduce the level of both random noise and periodic noise from a speech waveform signal to which noise has been added.
以下、図に従つて本発明を説明する。第１図は
本発明の方式の原理を説明するための、種々の波
形とその短時間自己相関数の関係の例、第２図は
本発明の方式の処理過程の流れ図、第３図は第２
図の各段階の波形を示した図である。第１図で１
は音声波形（母音）、３はランダム雑音波形、５
は周期性雑音波形、２，４，６はそれぞれ１，
３，５の短時間自己相関関数７は１と５が加わつ
た波形の短時間自己相関関数を平均した波形であ
る。第３図の１０，１１，１４，１２は１，２，
７に相当し、１３，１５は１１，１４から１２を
減じた波形、１６は出力波形である。 The present invention will be explained below with reference to the drawings. Fig. 1 is an example of the relationship between various waveforms and their short-time autocorrelation numbers to explain the principle of the method of the present invention, Fig. 2 is a flowchart of the processing process of the method of the present invention, and Fig. 3 is a flowchart of the processing process of the method of the present invention. 2
It is a figure which showed the waveform of each stage of a figure. 1 in Figure 1
is a speech waveform (vowel), 3 is a random noise waveform, 5 is
is a periodic noise waveform, 2, 4, and 6 are each 1,
The short-time autocorrelation function 7 of 3 and 5 is a waveform obtained by averaging the short-time autocorrelation functions of the waveforms to which 1 and 5 are added. 10, 11, 14, 12 in Figure 3 are 1, 2,
7, 13 and 15 are waveforms obtained by subtracting 12 from 11 and 14, and 16 is the output waveform.
最初に、第１図によつて種々の波形とその短時
間自己相関関数Ｓの関係を説明する。１は母音の
ような周期的音声波形であり、１のＳは２のよう
になり、１と２の周波数成分は等しい。なお。標
本化周期Ｉで標本化された入力信号をaiとする
と、Ｓは(1)式で計算される。 First, the relationship between various waveforms and their short-time autocorrelation function S will be explained with reference to FIG. 1 is a periodic speech waveform like a vowel, S of 1 becomes like 2, and the frequency components of 1 and 2 are equal. In addition. If the input signal sampled at the sampling period I is a i , S is calculated using equation (1).
Ｓ(j)＝１／ＭN
〓i=1
ai・ai+j ｊ＝０，１，２…，Ｌ−１ (1)
ｊは遅延時間に対応する標本番号、積分の時間は
NIである。ところで、ランダム雑音３のＳは４
のようになり、Ｎを大きくするとインパルスに近
づく。また、周期性雑音を５とすると、どの時刻
に関してもそのＳは６である。 S(j)=1/M N 〓 i=1 a i・a i+j j=0,1,2...,L−1 (1) j is the sample number corresponding to the delay time, and the integration time is
It is NI. By the way, S of random noise 3 is 4
As N becomes larger, it approaches an impulse. Further, if the periodic noise is 5, its S is 6 at any time.
ところで、SPACでは、準周期的とみなされる
音声波形のＳは２のような波形になるが、ランダ
ム雑音のＳが４のようなインパルス状になること
に着目し、ｊ＝０近傍を除いてＳの１周期の波形
を切り出して出力信号としている。その結果、１
に３が加わつた波形の場合、ランダム雑音成分を
大幅に減少した出力信号を得ている。一方、１に
５が加わつた場合、そのＳは２と６の和になり、
５の成分を除くことはできない。しかし、音声信
号を長時間観測すると定常な部分はわずかであ
り、波形を区切つて重ね合わせ平均すると、３の
ようなランダム雑音に近くなることはよく知られ
ている。したがつて、音声のＳを数多く平均する
と、４に近くなることが期待できる。一方、５の
ような周期信号のＳは、常に同じ形をしているの
で、平均してもその形は変わらない。そこで、１
周期ごとに計算されるＳから、Ｓを平均した波形
Ｓを差し引くと、Ｓに含まれている周期性雑音成
分を大幅にとり除くことができる。 By the way, in SPAC, the S of a speech waveform that is considered to be quasi-periodic has a waveform like 2, but we focused on the fact that the S of random noise has an impulse shape like 4, and except for the vicinity of j = 0, The waveform of one cycle of S is cut out and used as an output signal. As a result, 1
In the case of a waveform in which 3 is added to , an output signal with significantly reduced random noise components is obtained. On the other hand, if 5 is added to 1, S becomes the sum of 2 and 6,
Component 5 cannot be removed. However, it is well known that when an audio signal is observed for a long time, there are only a few stationary parts, and when the waveforms are segmented and averaged over each other, the resulting signal becomes close to random noise like 3. Therefore, if you average S of many voices, you can expect it to be close to 4. On the other hand, since S of a periodic signal such as 5 always has the same shape, its shape does not change even if it is averaged. Therefore, 1
By subtracting the waveform S obtained by averaging S from S calculated for each period, the periodic noise component included in S can be largely removed.
本発明の方式を第２図の流れ図と、第３図の波
形により説明する。第２図で、入力には音声波形
１と周期性雑音波形５が加わつた波形１０が、標
本化周期Ｉの時系列信号として加えられる。１０
に関して、時刻t1を基準にして(1)式により短時間
自己相関関数S1(j)１１を計算する。なお、(1)式で
は、t1に対応する標本をa1としている。次に１１
からＳ(j)を長時間にわたつて平均した1を(j)１
２を減じ、G1(j)１３を求める。この処理の結果、
１３では１１に含まれていた周期性雑音成分がほ
とんど取り除かれている。次に１３の極大値を求
め、その標本の遅延時間から周期T1を決定し、
１３からT1に相当する標本を切り出して出力信
号１６とする。なお、この標本の切出しに際して
は、ｊ＝０近傍の標本は使用しない。第２図で
は、ｊが０に近く、振幅が０から立ち上る標本か
ら、１周期の標本を切り出している。これは、１
０に含まれるランダム雑音成分が、４で示したよ
うにｊ＝０の付近に集まり、その影響が１３にも
残る可能性があるので行う処理である。 The system of the present invention will be explained with reference to the flowchart in FIG. 2 and the waveforms in FIG. 3. In FIG. 2, a waveform 10 in which a speech waveform 1 and a periodic noise waveform 5 are added is added as a time-series signal with a sampling period I to the input. 10
, a short-time autocorrelation function S 1 (j)11 is calculated using equation (1) using time t 1 as a reference. Note that in equation (1), the sample corresponding to t 1 is a 1 . Next 11
1 obtained by averaging S(j) over a long period of time from (j)1
Subtract 2 to find G 1 (j)13. As a result of this process,
In No. 13, most of the periodic noise components contained in No. 11 have been removed. Next, find the maximum value of 13, determine the period T 1 from the delay time of that sample,
A sample corresponding to T 1 is cut out from 13 and used as an output signal 16. Note that when cutting out this sample, samples near j=0 are not used. In FIG. 2, one period of samples is cut out from samples where j is close to 0 and the amplitude rises from 0. This is 1
This process is performed because the random noise components included in j=0 may gather around j=0 as shown in 4, and the influence may remain on j=0 as well.
次に、１０について、時刻t2（t2＝t1＋T1）を基
準にして短時間自己相関関数S2(j)１４を計算す
る。１４からＳ(j)を長時間にわたつて平均したS2
(j)；（１２とほとんど同じである。）を減じ、G2
(j)１５を求める。１５から周期T2を決定し、T2
に相当する標本を切り出し、１３から切り出した
標本に接続して出力信号１６とする。次に、１０
について、時刻t3（t3＝t2＋T2）を基準にして同様
の処理を行う。この操作を反復することにより、
１６には１０の中に含まれているランダム雑音と
周期性雑音成分を大幅に低減した信号を得ること
ができる。 Next, for 10, a short-time autocorrelation function S 2 (j) 14 is calculated using time t 2 (t 2 =t 1 +T 1 ) as a reference. 14 to S(j) averaged over a long time S 2
(j); (almost the same as 12), subtract G 2
(j) Find 15. Determine the period T 2 from 15 and T 2
A sample corresponding to is cut out and connected to the sample cut out from 13 to provide an output signal 16. Next, 10
Similar processing is performed for the time t 3 (t 3 =t 2 +T 2 ). By repeating this operation,
In 16, it is possible to obtain a signal in which the random noise and periodic noise components contained in 10 are significantly reduced.
以上の説明は有声音に対するものであるが、無
声音の場合はＳ(j)の極大値から適当に周期を定
め、有声音と同様の処理を行えばよい。 The above explanation is for voiced sounds, but for unvoiced sounds, the period may be determined appropriately from the maximum value of S(j) and the same processing as for voiced sounds may be performed.
なお、本発明の方式において、Ｉは標本化定理
を満足するように設定する。また、積分の時間
NIは20ms前後、LIも20ms程度になるように、Ｎ
やＬを設定すればよい。 Note that in the method of the present invention, I is set so as to satisfy the sampling theorem. Also, the time of integration
Set N so that NI is around 20ms and LI is around 20ms.
or L may be set.
一方、Ｓ(j)の長時間平均値(j)には幾つかの計
算法が考えられる。第１の方式は、時刻tkに関し
て(2)式で(j)を求め、処理を行う。 On the other hand, several calculation methods can be considered for the long-term average value (j) of S(j). The first method calculates (j) using equation (2) with respect to time t k and performs processing.
ｋ(j)＝１／ＭM
〓m=1
Sk-n(j) (2)
この方式は、常にtkよりも過去のＭ個のＳ(j)の平
均値を使用する。これは移動平均であり、周期性
雑音がゆるやかに変動する場合でも、比較的容易
に追従することができる。ただし、平均操作によ
り音声のみのを４のような形にするためにはＭ
を大きくとる必要がある。例えばＭ＝200とする
と、常に200のＳ(j)を記憶するメモリが必要であ
る。 k(j)=1/M M 〓 m=1 S kn (j) (2) This method always uses the average value of M S(j) past t k . This is a moving average and can be tracked relatively easily even if the periodic noise fluctuates slowly. However, in order to make the audio only into a shape like 4 by averaging operation, M
It is necessary to take a large value. For example, if M=200, a memory is required to always store 200 S(j).
ところで、周期性雑音が定常な場合、あるいは
極めてゆるやかに変化する場合は、Ｍ個の平均の
Ｓ(j)を、次のＭ周期の間使用しても実用上差し支
えない。換言すると、時刻tpに関して(3)式でSp(j)
を
p(j)＝１／Ｍ∞
〓m=1
Sp-n(j) ｐ＝Ｍ，2M，3M，…… (3)
を計算する。Sk(j)からは、ｋに最も近いｐ（ｋ
ｐ）のSp(j)を減じて処理を行う。これを第２の方
式とする。この方式は、第１の方式の場合より
も、周期性雑音の変動に対する追従が悪く、ま
た、処理を開始して最初のＭ周期の時間が経過す
るまで、周期性雑音の低減は行われないが、２種
類のP(j)；現在使つているものと次に使うため
に計算中のものを記憶しておくだけでよい。 By the way, if the periodic noise is stationary or changes very slowly, there is no practical problem in using M averages of S(j) for the next M periods. In other words, with respect to time t p, S p (j) in equation (3)
Calculate p (j)=1/M ∞ 〓 m=1 S pn (j) p=M, 2M, 3M, ... (3). From S k (j), p(k
Processing is performed by subtracting S p (j) of p). This is the second method. This method has poorer tracking of periodic noise fluctuations than the first method, and the periodic noise is not reduced until the first M periods have elapsed since the start of processing. However, you only need to remember two types of P (j); the one you are currently using and the one you are calculating for next time.
第３の方式として、(2)式と(3)式の(j)の中間的
な平均値として、時定数をもつた荷重平均が考え
られる。これを(4)式で示す。 As a third method, a weighted average with a time constant can be considered as an intermediate average value between (j) of equations (2) and (3). This is shown in equation (4).
k(j)＝Ｂ∞
〓m=1
Am-1・Sk-n(j)＝Ｂ（Sk-1(j)＋Ａ・k-1(j)） (4)
ここで、Ａは減衰定数（０＜Ａ＜１）、Ｂは周
期信号のときに、Ｓ(j)と(j)を等しくするための
定数である。この(j)では、時間の経過とともに
古いＳ(j)の寄与が小さくなり、周期性雑音の変動
にも追従しやすい。(4)式の計算には、毎回積和計
算を必要とするが、メモリは(3)式を使用する方式
と同様で少ない。 k (j)=B ∞ 〓 m=1 A m-1・S kn (j)=B(S k-1 (j)+A・k-1 (j)) (4) Here, A is the attenuation constant (0<A<1), B is a constant for making S(j) and (j) equal when the signal is a periodic signal. In this (j), the contribution of the old S(j) becomes smaller as time passes, and it is easier to follow fluctuations in periodic noise. Calculation of equation (4) requires a product-sum calculation each time, but the memory is similar to the method using equation (3) and is small.
次に本発明の実施例について述べる。3.4kHzに
帯域制限した男声に、妨害波として800Hzの正弦
波を加え、Ｓ／Ｎがほぼ0dBになるような資料を
作成した。この資料を標本化周期0.1msで標本化
し、第２図の方法で処理を行つた。 Next, examples of the present invention will be described. We created a document that added an 800Hz sine wave as an interference wave to a male voice with the band limited to 3.4kHz, resulting in an S/N ratio of approximately 0dB. This material was sampled at a sampling period of 0.1 ms and processed using the method shown in Figure 2.
(1)式のＮは200、Ｌは170である。(2)式を使用す
る第１の方式で、Ｍを30から250まで変えて出力
信号を求めたところ、いずれの場合も妨害波の
800Hzはほとんど完全に除去され検知できなくな
つた。ただ、には音声のＳの平均値が成分とし
て含まれ、これはＭを無限大にすれば０になるも
のである。本実施例のようにＭが有限の場合は雑
音となり、検知されるが入力信号に比較するとは
るかにききやすい。Ｍが200程度（平均の周期は
8ms位）のとき、出力音声信号に対してこの雑音
レベルは−13〜−14dBであり、実質的な妨害の
度合は少ない。なお、更にＭを大きくとれば雑音
レベルは低くなるが、妨害波の周期波を除去する
までに時間がかかるため、通常は１〜３秒の区間
でを求めるようにＭを設定すればよい。 次に
(3)式を使用する第２の方式について同様に実験を
行つた。この場合、最初のSpが計算される（Ｍ
周期の時間が経過する。）まで、周期性雑音の減
少は行われないが、その後は第１の方式と同様の
効果をあげた。 In equation (1), N is 200 and L is 170. In the first method using equation (2), the output signal was obtained by varying M from 30 to 250, and in each case, the interference wave was
800Hz was almost completely removed and became undetectable. However, the average value of S of the voice is included as a component, and this becomes 0 if M is set to infinity. When M is finite as in this embodiment, it becomes noise and is detected, but it is much easier to hear than the input signal. M is about 200 (the average period is
8 ms), this noise level is -13 to -14 dB with respect to the output audio signal, and the degree of substantial interference is small. It should be noted that if M is made larger, the noise level will be lowered, but since it takes time to remove the periodic waves of the interference wave, it is usually sufficient to set M so that it is determined in an interval of 1 to 3 seconds. next
A similar experiment was conducted regarding the second method using equation (3). In this case, the first Sp is calculated (M
The period of time passes. ), the periodic noise was not reduced, but after that, the same effect as the first method was achieved.
次に(4)式を使用する第３の方式について同様の
実験を行つた。ここで、Ａ＝0.98、Ｂ＝１／49と
したところ、周期性雑音は徐々に減衰し、約２秒
経過してからは、ほとんど検知できなくなつた。
その後は、第１の方式と同様の効果をあげた。 Next, a similar experiment was conducted regarding a third method using equation (4). Here, when A=0.98 and B=1/49, the periodic noise gradually attenuated and became almost undetectable after about 2 seconds.
After that, the same effect as the first method was obtained.
以上のように、本発明によれば、従来のランダ
ム性雑音の低減に加えて、音声信号に加わつた周
期性雑音を大幅に低減することができるから、本
発明は騒音環境下の音声通信や、無線通信におけ
る通話品質の改善に応く利用することができる。 As described above, according to the present invention, in addition to the conventional reduction of random noise, it is possible to significantly reduce periodic noise added to voice signals. can be used to improve call quality in wireless communications.
第１図と第３図は本発明の原理を説明する波
形、第２図は本発明の実施例の流れ図である。
1 and 3 are waveforms illustrating the principle of the present invention, and FIG. 2 is a flowchart of an embodiment of the present invention.
Claims (1)
関数Ｓに変換し、Ｓの１周期の波形を逐次接続し
て出力信号とする音声処理方式において、過去の
Ｍ（Ｍ２の整数）個のＳの平均値をＳから減じ
てから波形を接続することにより、入力音声波形
信号に重なつたランダム雑音成分と周期性雑音成
分を大幅に減少した音声波形信号を得ることを特
徴とする雑音レベル低減方式。 ２ 音声波形信号を１周期ごとに短時間自己相関
関数Ｓに変換し、Ｓの１周期の波形を逐次接続し
て出力信号とする音声処理方式において、Ｍ個の
Ｓの平均値を求め、この平均値を次のＭ個のＳか
らそれぞれ減じてから波形を接続して出力信号と
し、この過程をＭ周期ごとに反復することによ
り、入力音声波形信号に重なつたランダム雑音成
分と周期性雑音成分を大幅に減少した音声波形信
号を得ることを特徴とする雑音レベル低減方式。 ３ 音声波形信号を１周期ごとに短時間自己相関
関数Ｓに変換し、Ｓの１周期の波形を逐次接続し
て出力信号とする音声処理方式において、ｍ周期
（ｍ１）前のＳに、（ｍ−１）回係数Ａ（０＜Ａ
＜１）を乗じて累積加算して求めたＳの荷重平均
値を、Ｓから減じてから波形を接続することによ
り、入力音声波形に重なつたランダム雑音成分と
周期性雑音成分を大幅に減少した音声波形信号を
得ることを特徴とする雑音レベル低減方式。[Claims] 1. In an audio processing method in which an audio waveform signal is converted into a short-time autocorrelation function S every cycle, and the waveforms of one cycle of S are successively connected to produce an output signal, the past M(M2 By subtracting from S the average value of S (an integer of Features a noise level reduction method. 2. In an audio processing method in which an audio waveform signal is converted into a short-time autocorrelation function S for each period, and the waveforms of one period of S are successively connected to produce an output signal, the average value of M S is determined, and the average value of M S is calculated. By subtracting the average value from each of the next M S and then connecting the waveforms to obtain the output signal, repeating this process every M periods, the random noise component and periodic noise superimposed on the input speech waveform signal are A noise level reduction method characterized by obtaining an audio waveform signal with significantly reduced components. 3 In an audio processing method in which an audio waveform signal is converted into a short-time autocorrelation function S every cycle, and the waveforms of one cycle of S are sequentially connected to produce an output signal, ( m-1) times coefficient A (0<A
By subtracting the weighted average value of S obtained by multiplying and cumulatively adding <1) from S and then connecting the waveforms, the random noise component and periodic noise component superimposed on the input speech waveform can be significantly reduced. A noise level reduction method characterized by obtaining a voice waveform signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP14196783A JPS6033752A (en) | 1983-08-04 | 1983-08-04 | Noise level reduction system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP14196783A JPS6033752A (en) | 1983-08-04 | 1983-08-04 | Noise level reduction system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPS6033752A JPS6033752A (en) | 1985-02-21 |
| JPH0336346B2 true JPH0336346B2 (en) | 1991-05-31 |
Family
ID=15304287
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP14196783A Granted JPS6033752A (en) | 1983-08-04 | 1983-08-04 | Noise level reduction system |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPS6033752A (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6136995B2 (en) | 2014-03-07 | 2017-05-31 | 株式会社Ｊｖｃケンウッド | Noise reduction device |
| PL226101B1 (en) | 2015-02-10 | 2017-06-30 | Sławomir Szechniuk | Method for reduction of interference and noise in the circuits with two signal lines and the phrase filter |
-
1983
- 1983-08-04 JP JP14196783A patent/JPS6033752A/en active Granted
Also Published As
| Publication number | Publication date |
|---|---|
| JPS6033752A (en) | 1985-02-21 |
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