CN201716985U - Automatic cough identification device - Google Patents
Automatic cough identification device Download PDFInfo
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- CN201716985U CN201716985U CN2010202475290U CN201020247529U CN201716985U CN 201716985 U CN201716985 U CN 201716985U CN 2010202475290 U CN2010202475290 U CN 2010202475290U CN 201020247529 U CN201020247529 U CN 201020247529U CN 201716985 U CN201716985 U CN 201716985U
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Abstract
The utility model provides an automatic cough identification device which comprises a sound input device, an identifier, an SD card, a display device and a group of buttons, wherein the sound input device, the SD card, the display device and the buttons are connected with the identifier respectively; the sound input device comprises a microphone and an encoder used for converting a sound input into a sound signal in PCM format, the microphone is connected with the encoder, and the encoder is connected with the identifier; the identifier comprises an end-point detection unit used for end-point detection, a feature extraction unit used for extracting a feature vector sequence and an identification unit used for cough identification according to the extracted feature vector sequence; and the end-point detection unit, the feature extraction unit and the identification unit are connected in sequence and all connected with the buttons. The utility model has characteristics of recording the cough sound in real time, automatically identifying the cough sound and providing the dynamic change in cough frequency and intensity.
Description
Technical field
The utility model belongs to the acoustic processing technical field, particularly a kind of cough automatic identification equipment.
Background technology
Chronic cough is that reason is not bright, cough surpasses 8 weeks, chest X-ray Non Apparent Abnormality person for only symptom or cardinal symptom, time.The acidophil bronchitis is the first reason (22%) of chronic cough, and other common diseases are because of comprising: water clock syndrome (17%), CVA (14%), gastroesophageal reflux induced cough (12%) etc. behind the nose).Want the diagnosis of clear and definite chronic cough, need technology such as pulmonary function instrument, the inspection of induction of sputum cytological classification and the monitoring of esophageal pH value, present most of hospitals do not possess these conditions, and the chronic cough patient can not get diagnosis and treatment timely; Simultaneously, characteristics of coughing fit (comprising the dynamic change of frequency and intensity) and inducement have very big relation with weather, environment, diet and sleep state etc., if can find out coughing fit and weather, environment, diet and dormant relation, can help to understand the cause of disease of chronic cough; But; Most patients can not be described the characteristics of coughing fit accurately, objectively; and in the existing technology; do not have correlation technique or equipment can obtain accurately, describe objectively the characteristics of coughing fit yet, therefore usually can cause influencing timely diagnosis and treatment patient because of the prescription on individual diagnosis doctor can not get effective information to the coughing fit cause of disease.
The utility model content
Primary and foremost purpose of the present utility model is to overcome the shortcoming of above-mentioned prior art with not enough, and providing a kind of can carry out patient's signal Processing of coughing by sound, and can obtain the cough signal exactly, and the cough automatic identification equipment that can add up the cough signal
For reaching above-mentioned purpose, the utility model adopts following technical scheme:
A kind of cough automatic identification equipment comprises:
Be used for sound import and measure sound pressure variations, measurement result is converted into the acoustic input dephonoprojectoscope of the voice signal of PCM form;
Be used for the voice signal of PCM form is carried out the recognizer of end-point detection, feature extraction and cough identification;
The SD card that is used for storage of speech signals;
Be used to show the display device of sound input, status recognition and recognition result;
One group of button that is used to control sound input and cough identification;
Described acoustic input dephonoprojectoscope, SD card, display device, button are connected with recognizer respectively.
The sound that described acoustic input dephonoprojectoscope comprises microphone and is used for importing is converted into the scrambler of the voice signal of PCM form, and described microphone is connected with scrambler, and described scrambler is connected with recognizer.
Described recognizer is made of the processor, random access memory and the erasable and programable memory that connect successively.Processor in the described recognizer comprises the end-point detection unit that is used to realize end-point detection, the recognition unit that is used to extract the feature extraction unit of characteristic vector sequence and is used to utilize the characteristic vector sequence realization cough identification of being extracted; Described end-point detection unit, feature extraction unit are connected successively with recognition unit, and all are connected with described button.Analysis button is pressed, and described end-point detection unit, feature extraction unit and recognition unit move successively.
Be provided with by one group of 26 passage in the described feature extraction unit, to have 32ms window and pre-correction factor be the bank of filters that 0.97 Mel frequency filter constitutes.
Described button comprises and is used for analysis button that the sound of input is recorded and it is saved to the record button of SD card and is used to start the cough discriminance analysis; Described record button all is connected with recognizer with analysis button.
Described display device is a display screen.
The performing step of said apparatus is specific as follows:
(1) acoustic input dephonoprojectoscope is measured sound pressure variations, and transfers measurement result the voice signal of PCM form to by scrambler, deposits the SD card in;
(2) the end-point detection unit carries out end-point detection to the voice signal of the PCM form in the described step (1), rejects non-cough signal, and all the other signals are as candidate's signal of coughing;
(3) feature extraction unit is extracted feature frame by frame to candidate's signal of coughing, and is converted into characteristic vector sequence; Described characteristic vector sequence is preferably the cepstrum sequence vector of one 39 dimensions;
Specifically, feature extraction unit with described step (2) divide candidate behind the frame cough signal through the Mel frequency filter of one group of 26 passage to handling, obtain the MFCC parameter (Mel frequency cepstral coefficient) of one 12 dimension, add short-time energy, constitute 13 dimensional vectors as additional parameter; Simultaneously, be to describe the correlativity between the signal frame and the behavioral characteristics of cough, introduce one, second order difference, form the characteristic vector sequence of one 39 dimensions.
The duration difference because each time coughed, therefore, candidate's signal of coughing finally is converted to characteristic vector sequence frame by frame, and for example the duration is that the candidate of n frame coughs that to correspond to n row dimension be 39 characteristic vector sequence to sequence;
(4) the characteristic vector sequence training hidden Markov model that extracted according to step (3) of recognition unit is discerned candidate's signal of coughing, and judges whether to be the cough signal;
(5) the cough signal of described step (4) cough identification gained in the certain hour section is added up.
End-point detection in the described step (2) comprises the steps:
(2-1) voice signal with the PCM form of step (1) carries out the branch frame;
(2-2) adopt hamming code window that each frame signal of described step (2-1) gained is carried out windowing process, and calculate the short-time zero-crossing rate in this frame, constitute the set of candidate's zero-crossing rate threshold value; Described short-time zero-crossing rate satisfies:
, wherein, Z
nBe short-time zero-crossing rate, x (.) is the voice signal of PCM form of input, w (.) and sgn[x] to be respectively be hamming code window function and sign function, N=512;
(2-3) determine the zero-crossing rate threshold value;
(2-4) cough signal and non-cough signal of identification candidate, if short-time zero-crossing rate is greater than the zero-crossing rate threshold value, then this frame is regarded as candidate's signal of coughing, otherwise is regarded as non-cough signal and the zero setting rejecting;
(2-5) satisfy short-time zero-crossing rate greater than the zero-crossing rate threshold value, then these frames are merged into one section voice signal,, write down its starting point and terminating point as candidate's signal of coughing if continue the number frames.
The branch frame step of described step (2-1) is: with the frame is the voice signal that unit reads the PCM form, and when fetching data, the overlapping of former frame and back one frame partly moves for frame.
Frame length is got 32ms, promptly corresponding 512 sampled values; Described frame moves and is 8ms, promptly corresponding 128 sampled values.
Definite step of the zero-crossing rate threshold value in the described step (2-3) is:
(2-3-1) short-time zero-crossing rate that described step (2-2) is obtained rearranges by from small to large order, is designated as { Z
1, Z
2..., Z
K;
(2-3-2) determine expectation end-point detection False Rate, choose corresponding short-time zero-crossing rate as the zero-crossing rate threshold value in described step (2-3-1), described zero-crossing rate threshold value is at step (2-3-1) gained set { Z
1, Z
2..., Z
KThe arrangement sequence number satisfy: h=int (ε * K),
Wherein, ε is set { Z for expectation end-point detection False Rate, K
1, Z
2..., Z
KElement number; H is that the zero-crossing rate threshold value is at { Z
1, Z
2..., Z
KThe arrangement sequence number.
In the described step (4) to the candidate cough signal cough identification concrete steps be:
Be the candidate of the N signal of coughing to frame number (4-1), if N=1, then the cough similarity value of signal of candidate is zero; If N=2, then get the proper vector of 1～2 frame signal, mend a frame dimension again and be 13 null vector proper vector as the 3rd frame, utilize the hidden Markov model that trains to discern, recognition result is as the cough similarity of signal of candidate; If N＞2 then repeat N-2 identification, wherein discern the proper vector of getting i～i+2 frame the i time, utilize the hidden Markov model that trains to discern, recognition result is designated as p
iGet p=max{p at last
1, p
2..., p
N-2As the cough similarity of signal of candidate;
(4-2) determine similarity threshold;
(4-3) candidate of each frame signal of coughing is discerned, if the similarity of this frame is greater than similarity threshold, then this section candidate signal of coughing is the cough signal, otherwise is non-cough signal.
Definite step of the similarity threshold in the described step (4-2) is:
(4-2-1) similarity that described step (4-1) is obtained rearranges by from small to large order, is designated as { λ
1, λ
2..., λ
M;
(4-2-2) determine expectation identification False Rate, choose corresponding similarity as similarity threshold in described step (4-2-1), described similarity threshold is at step (4-2-1) gained set { λ
1, λ
2..., λ
MThe arrangement sequence number satisfy: l=int (ξ * M);
Wherein, ξ is set { λ for expectation identification False Rate, M
1, λ
2..., λ
MElement number; L is that similarity threshold is at { λ
1, λ
2..., λ
MThe arrangement sequence number.
Described step (5) is specially: per half an hour the statistics intensity of once coughing, and with the absolute value that adds window signal as signal intensity, with the signal intensity average of the frame of all cough signals in per half an hour as this period cough intensity.
The utility model can be by real time record and automatic identification cough sound, in conjunction with patient's life diary, can understand the characteristics of coughing fit and inducement and with weather, environment, diet and dormant relation, the doctor's that helps going to a doctor timely diagnosis and treatment.
Compared with prior art, the utlity model has following advantage and beneficial effect:
1, the utility model can be to patient the cough real time record of signal and automatically identification, the dynamic change characteristic of cough frequency and intensity is provided, help the relation that the doctor understands coughing fit and factors such as environment, weather, diet and sleep, can make that the cough patient is treated timely.
2, the utility model can be added up patient's signal of coughing, and can show by display screen, has advantages such as hommization, statistics be accurate.
Description of drawings
Fig. 1 is the structural representation of the utility model device.
Fig. 2 is the realization flow synoptic diagram of device shown in Figure 1.
Embodiment
Below in conjunction with embodiment and accompanying drawing the utility model is described in further detail, but embodiment of the present utility model is not limited thereto.
Embodiment
As shown in Figure 1, this cough automatic identification equipment comprises:
Be used for sound import and measure sound pressure variations, measurement result is converted into the acoustic input dephonoprojectoscope of the voice signal of PCM form;
Be used for the voice signal of PCM form is carried out the recognizer 14 of end-point detection, feature extraction and cough identification;
The SD card 13 that is used for storage of speech signals;
Be used to show the display device of sound input, status recognition and recognition result;
One group of button that is used to control sound input and cough identification;
Described acoustic input dephonoprojectoscope, SD card 13, display device, button are connected with recognizer 14 respectively.
Described acoustic input dephonoprojectoscope comprises that microphone 10 and the sound that is used for importing are converted into the scrambler 16 of the voice signal of PCM form, and described microphone 10 is connected with scrambler 16, and described scrambler 16 is connected with recognizer 14.
Described recognizer 14 is made of the processor, random access memory and the erasable and programable memory that connect successively.Processor in the described recognizer 14 comprises the end-point detection unit that is used to realize end-point detection, the recognition unit that is used to extract the feature extraction unit of characteristic vector sequence and is used to utilize the characteristic vector sequence realization cough identification of being extracted; Described end-point detection unit, feature extraction unit are connected successively with recognition unit, and all are connected with described button.
Be provided with by one group of 26 passage in the described feature extraction unit, to have 32ms window and pre-correction factor be the bank of filters that 0.97 Mel frequency filter constitutes.
Described button comprises and is used for analysis button 12 that the voice signal of input is recorded and it is saved to the record button 11 of SD card 13 and is used to start the cough discriminance analysis; Described record button 11 all is connected with recognizer 14 with analysis button 12; Analysis button 12 is pressed, and described end-point detection unit, feature extraction unit and recognition unit move successively.
Described display device is a display screen 15.
As shown in Figure 2, the step of said apparatus realization is specific as follows:
(1) acoustic input dephonoprojectoscope is measured sound pressure variations, and transfers measurement result the voice signal of PCM form to by scrambler, deposits the SD card in;
(2) the end-point detection unit carries out end-point detection to the voice signal of the PCM form in the described step (1), rejects non-cough signal, and all the other signals are as candidate's signal of coughing;
(3) feature extraction unit is extracted feature frame by frame to candidate's signal of coughing, and is converted into characteristic vector sequence; Described characteristic vector sequence is the cepstrum sequence vector of one 39 dimensions;
Specifically, feature extraction unit with described step (2) divide candidate behind the frame cough signal through the Mel frequency filter of one group of 26 passage to handling, obtain the MFCC parameter (Mel frequency cepstral coefficient) of one 12 dimension, add short-time energy, constitute 13 dimensional vectors as additional parameter; Simultaneously, be to describe the correlativity between the signal frame and the behavioral characteristics of cough, introduce one, second order difference, form the characteristic vector sequence of one 39 dimensions.
The duration difference because each time coughed, therefore, candidate's signal of coughing finally is converted to characteristic vector sequence frame by frame, and for example the duration is that the candidate of n frame coughs that to correspond to n row dimension be 39 characteristic vector sequence to sequence;
(4) the characteristic vector sequence training hidden Markov model that extracted according to step (3) of recognition unit is discerned candidate's signal of coughing, and judges whether to be the cough signal;
(5) the cough signal of described step (4) cough identification gained in the certain hour section is added up.
End-point detection in the described step (2) comprises the steps:
(2-1) voice signal with the PCM form of step (1) carries out the branch frame;
(2-2) adopt hamming code window that each frame signal of described step (2-1) gained is carried out windowing process, and calculate the short-time zero-crossing rate in this frame, constitute the set of candidate's zero-crossing rate threshold value; Described short-time zero-crossing rate satisfies:
Wherein, Z
nBe short-time zero-crossing rate, x (.) is the voice signal of PCM form of input, w (.) and sgn[x] to be respectively be hamming code window function and sign function, N=512;
(2-3) determine the zero-crossing rate threshold value;
(2-4) cough signal and non-cough signal of identification candidate, if short-time zero-crossing rate is greater than the zero-crossing rate threshold value, then this frame is regarded as candidate's signal of coughing, otherwise is regarded as non-cough signal and the zero setting rejecting;
(2-5) satisfy short-time zero-crossing rate greater than the zero-crossing rate threshold value, then these frames are merged into one section voice signal,, write down its starting point and terminating point as candidate's signal of coughing if continue the number frames.
The branch frame step of described step (2-1) is: with the frame is the voice signal that unit reads the PCM form, and when fetching data, the overlapping of former frame and back one frame partly moves for frame.
Frame length is got 32ms, promptly corresponding 512 sampled values; Described frame moves and is 8ms, promptly corresponding 128 sampled values.
Definite step of the zero-crossing rate threshold value in the described step (2-3) is:
(2-3-1) short-time zero-crossing rate that described step (2-2) is obtained rearranges by from small to large order, is designated as { Z
1, Z
2..., Z
K;
(2-3-2) determine expectation end-point detection False Rate, choose corresponding short-time zero-crossing rate as the zero-crossing rate threshold value in described step (2-3-1), described zero-crossing rate threshold value is at step (2-3-1) gained set { Z
1, Z
2..., Z
KThe arrangement sequence number satisfy: h=int (ε * K),
Wherein, ε is set { Z for expectation end-point detection False Rate, K
1, Z
2..., Z
KElement number; H is that the zero-crossing rate threshold value is at { Z
1, Z
2..., Z
KThe arrangement sequence number.
In the described step (4) to the candidate cough signal cough identification concrete steps be:
Be the candidate of the N signal of coughing to frame number (4-1), if N=1, then the cough similarity value of signal of candidate is zero; If N=2, then get the proper vector of 1～2 frame signal, mend a frame dimension again and be 13 null vector proper vector as the 3rd frame, utilize the hidden Markov model that trains to discern, recognition result is as the cough similarity of signal of candidate; If N＞2 then repeat N-2 identification, wherein discern the proper vector of getting i～i+2 frame the i time, utilize the hidden Markov model that trains to discern, recognition result is designated as p
iGet p=max{p at last
1, p
2..., p
N-2As the cough similarity of signal of candidate;
(4-2) determine similarity threshold;
(4-3) candidate of each frame signal of coughing is discerned, if the similarity of this frame is greater than similarity threshold, then this section candidate signal of coughing is the cough signal, otherwise is non-cough signal.
Definite step of the similarity threshold in the described step (4-2) is:
(4-2-1) similarity that described step (4-1) is obtained rearranges by from small to large order, is designated as { λ
1, λ
2..., λ
M;
(4-2-2) determine expectation identification False Rate, choose corresponding similarity as similarity threshold in described step (4-2-1), described similarity threshold is at step (4-2-1) gained set { λ
1, λ
2..., λ
MThe arrangement sequence number satisfy: l=int (ξ * M);
Wherein, ξ is set { λ for expectation identification False Rate, M
1, λ
2..., λ
MElement number; L is that similarity threshold is at { λ
1, λ
2..., λ
MThe arrangement sequence number.
Described step (5) is specially: per half an hour the statistics intensity of once coughing, and with the absolute value that adds window signal as signal intensity, with the signal intensity average of the frame of all cough signals in per half an hour as this period cough intensity.
The foregoing description is the utility model preferred implementation; but embodiment of the present utility model is not restricted to the described embodiments; other any do not deviate from change, the modification done under spirit of the present utility model and the principle, substitutes, combination, simplify; all should be the substitute mode of equivalence, be included within the protection domain of the present utility model.
Claims (7)
- One kind the cough automatic identification equipment, it is characterized in that comprising:Be used for sound import and measure sound pressure variations, measurement result is converted into the acoustic input dephonoprojectoscope of the voice signal of PCM form;Be used for the voice signal of PCM form is carried out the recognizer of end-point detection, feature extraction and cough identification;The SD card that is used for storage of speech signals;Be used to show the display device of sound input, status recognition and recognition result;One group of button that is used to control sound input and cough identification;Described acoustic input dephonoprojectoscope, SD card, display device, button are connected with recognizer respectively.
- 2. cough automatic identification equipment according to claim 1, it is characterized in that: described acoustic input dephonoprojectoscope comprises microphone and is used for the sound of input is converted into the scrambler of the voice signal of PCM form, described microphone is connected with scrambler, and described scrambler is connected with recognizer.
- 3. cough automatic identification equipment according to claim 1 is characterized in that: described recognizer is made of the processor, random access memory and the erasable and programable memory that connect successively.
- 4. cough automatic identification equipment according to claim 3 is characterized in that: described processor comprises the end-point detection unit that is used to realize end-point detection, the recognition unit that is used to extract the feature extraction unit of characteristic vector sequence and is used to utilize the characteristic vector sequence realization cough identification of being extracted; Described end-point detection unit, feature extraction unit are connected successively with recognition unit, and all are connected with described button.
- 5. cough automatic identification equipment according to claim 4 is characterized in that: be provided with by one group of 26 passage in the described feature extraction unit, to have 32ms window and pre-correction factor be the bank of filters that 0.97 Mel frequency filter constitutes.
- 6. the cough automatic identification equipment of stating according to claim 1 is characterized in that: described button comprises and is used for analysis button that the sound of input is recorded and it is saved to the record button of SD card and is used to start the cough discriminance analysis; Described record button all is connected with recognizer with analysis button.
- 7. cough automatic identification equipment according to claim 1 is characterized in that: described display device is a display screen.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010202475290U CN201716985U (en) | 2010-07-02 | 2010-07-02 | Automatic cough identification device |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010202475290U CN201716985U (en) | 2010-07-02 | 2010-07-02 | Automatic cough identification device |
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| CN201716985U true CN201716985U (en) | 2011-01-19 |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103474070A (en) * | 2012-11-07 | 2013-12-25 | 叶如康 | Sound transmission apparatus |
| CN103919531A (en) * | 2014-04-22 | 2014-07-16 | 广东小天才科技有限公司 | Health monitoring device and device with health monitoring device and for leaning of children |
| CN105213193A (en) * | 2015-09-25 | 2016-01-06 | 捷荣科技集团有限公司 | A kind of anti-milk device and anti-milk method of choking of choking |
| CN105513613A (en) * | 2015-11-30 | 2016-04-20 | 中国农业科学院农业信息研究所 | Livestock cough monitoring and early-warning method and device |
| CN109498228A (en) * | 2018-11-06 | 2019-03-22 | 林枫 | Lung recovery therapeutic equipment based on cough sound feedback |
-
2010
- 2010-07-02 CN CN2010202475290U patent/CN201716985U/en not_active Expired - Fee Related
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103474070A (en) * | 2012-11-07 | 2013-12-25 | 叶如康 | Sound transmission apparatus |
| CN103474070B (en) * | 2012-11-07 | 2016-06-08 | 宁波市鄞州乐可机电科技有限公司 | A kind of sound transmission device |
| CN103919531A (en) * | 2014-04-22 | 2014-07-16 | 广东小天才科技有限公司 | Health monitoring device and device with health monitoring device and for leaning of children |
| CN105213193A (en) * | 2015-09-25 | 2016-01-06 | 捷荣科技集团有限公司 | A kind of anti-milk device and anti-milk method of choking of choking |
| CN105213193B (en) * | 2015-09-25 | 2019-02-05 | 捷荣科技集团有限公司 | A kind of capable of preventing milk choking device and capable of preventing milk choking method |
| CN105513613A (en) * | 2015-11-30 | 2016-04-20 | 中国农业科学院农业信息研究所 | Livestock cough monitoring and early-warning method and device |
| CN109498228A (en) * | 2018-11-06 | 2019-03-22 | 林枫 | Lung recovery therapeutic equipment based on cough sound feedback |
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Granted publication date: 20110119 Termination date: 20130702 |