Methods and equipment for measuring sound signals.

Abstract

DEPCT63 This request discloses the methods and equipment for measuring audio signals to mitigate the problem of high processing rates. The resource consumption is relatively low compared to other methods of measuring audio signals in existing technologies. Including: noise signal reduction; splitting the noise signal into a certain number of short-term energy frames by... It is based on the defined audio signal frequency; the indication of the power of each short-term power frame; and Measurements are taken based on the energy of each short-term energy frame to determine whether the acoustic signal includes noise. -----------------------------------------------------------

Description

A Method and Device for Detecting Speech Signals Technical Field This application relates to the field of computer technologies, and particularly to a method and device for detecting speech signals. Background Art In real life, people often use intelligent devices (such as smart phones, tablet computers, etc.) to send voice messages. However, when people use intelligent devices to send voice messages, they often need to click the start or end button on the screen of the intelligent device to complete the sending of the voice message, and these click operations cause a lot of inconvenience to users. If users can complete the sending of voice messages without clicking buttons, then the intelligent device needs to keep recording or record according to a preset period, and determine whether the acquired audio signal contains a speech signal. If it contains a speech signal, the speech signal is extracted, and then processed and sent later, thus completing the sending of the voice message. In the prior art, speech signal detection methods such as the double-threshold method, the detection method based on the maximum of autocorrelation, or the detection method based on wavelet transform are generally used to detect whether the acquired audio signal contains a speech signal. However, these methods basically obtain the frequency characteristics of audio information through complex calculations such as Fourier transform, and then determine whether it contains a speech signal according to the frequency characteristics. They need to calculate a large amount of buffered data, occupy a high memory, have a large calculation amount, a slow processing speed, and a large power consumption. Summary of the Invention Embodiments of this application provide a method and device for detecting speech signals, which are used to solve the problems of slow processing speed and high resource consumption existing in the speech signal detection methods in the prior art. Embodiments of this application adopt the following technical solutions: A method for detecting speech signals, the method includes: Obtain an audio signal; Divide the audio signal into a plurality of short-time energy frames according to the frequency of a preset speech signal; Determine the energy of each short-time energy frame; Detect whether the audio signal contains a speech signal according to the energy of each short-time energy frame. A device for detecting speech signals, the device includes: An obtaining module, which obtains an audio signal; A dividing module, which divides the audio signal into a plurality of short-time energy frames according to the frequency of a preset speech signal; A determining module, which determines the energy of each short-time energy frame; A detecting module, which detects whether the audio signal contains a speech signal according to the energy of each short-time energy frame. The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: Compared with the existing detection methods that determine whether a voice signal is included in an audio signal through complex calculations such as Fourier transform, the voice signal detection method adopted in the embodiments of the present application does not require complex calculations such as Fourier transform. By dividing the obtained audio signal into multiple short-time energy frames according to the frequency of the preset voice signal, the energy of each short-time energy frame is then determined, and based on the energy of each short-time energy frame, it can be detected whether the obtained audio signal contains a voice signal. Therefore, the voice signal detection method provided by the embodiments of the present application can solve the problems of slow processing speed and high resource consumption existing in the existing voice signal detection methods. BRIEF DESCRIPTION OF THE DRAWINGS The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings: FIG. 1 is a specific flowchart of a voice signal detection method provided by an embodiment of the present application; FIG. 2 is a specific flowchart of another voice signal detection method provided by an embodiment of the present application; FIG. 3 is a display diagram of an audio signal with a preset duration provided by an embodiment of the present application; FIG. 4 is a specific structural schematic diagram of a voice signal detection device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application. The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the drawings. To solve the problems of slow processing speed and high resource consumption existing in the existing voice signal detection methods, the embodiments of the present application provide a voice signal detection method. The execution subject of this method can be, but is not limited to, user terminals such as mobile phones, tablet computers, or personal computers (PCs), or applications (APPs) running on these user terminals, or devices such as servers. For ease of description, in the following, an example where the execution entity of this method is an APP will be used to introduce the implementation manners of this method. It can be understood that taking the execution entity of this method as an APP is only an exemplary illustration and should not be construed as a limitation on this method. The schematic diagram of the specific process of this method is shown in Figure 1 and includes the following steps: Step 101, obtain an audio signal. The above-mentioned audio signal can be an audio signal collected by the APP through an audio collection device, or an audio signal received by the APP. For example, it can be an audio signal transmitted by other APPs or devices. The embodiments of the present application do not make any limitations on this. After the APP obtains the audio signal, it can save the audio signal locally. The present application also does not make any restrictions on the sampling rate, duration, format, or number of channels, etc. of the above-mentioned audio signal. The above APP can be any type of APP, such as a chat APP or a payment APP, etc., as long as the APP can obtain an audio signal and can use the voice signal detection method provided by the embodiments of the present application to detect the obtained audio signal. Step 102, divide the audio signal into a plurality of short-time energy frames according to the frequency of a preset voice signal. The above-mentioned short-time energy frames are actually part of the audio signal in the audio signal obtained in step 101. Specifically, the period of the preset voice signal can be determined according to the frequency of the preset voice signal, and according to the determined period, the audio signal obtained in step 101 is divided into a plurality of short-time energy frames with the same duration as the period. For example, assuming that the period of the preset voice signal is 0.01S, then according to the duration of the audio signal obtained in step 101, the audio signal can be divided into several short-time energy frames with a duration of 0.01S each. It should be noted that when dividing the audio signal obtained in step 101, according to the actual situation, the audio signal can also be divided into at least two short-time energy frames according to the frequency of the preset voice signal. For the convenience of subsequent description, in the embodiments of the present application, the example of dividing the audio signal into a plurality of short-time energy frames will be used for illustration hereinafter. In addition, when the APP itself collects the audio signal through the audio collection device in step 101, since the audio signal is generally collected as a digital signal at a certain sampling rate from an audio signal that is actually an analog signal, that is, an audio signal in Pulse Code Modulation (PCM) format, therefore, the audio signal can also be divided into a plurality of short-time energy frames according to the sampling rate of the audio signal and the frequency of the preset voice signal. Specifically, the ratio m of the sampling rate of the audio signal to the frequency of the preset speech signal can be determined, and then, according to the ratio m, every m sampling points in the acquired digital audio signal are divided into a short-time energy frame. If m is a positive integer, the audio signal can be divided into the maximum number of short-time energy frames according to m; if m is not a positive integer, the audio signal can be divided into the maximum number of short-time energy frames according to m rounded to a positive integer according to the rounding principle. It should be particularly noted that if the number of sampling points included in the audio signal obtained in step 101 is not an integer multiple of m, after the audio signal is divided into the maximum number of short-time energy frames, the remaining sampling points can be discarded, or the remaining sampling points can also be used as a short-time energy frame for subsequent processing. Among them, the above-mentioned m is used to represent the number of sampling points included in the audio signal obtained in step 101 within a period of a preset speech signal. For example, if the frequency of the preset speech signal is 82HZ, the duration of the audio signal obtained in step 101 is 1S, and the sampling rate is 16000HZ, then m = 16000 / 82 = 195.1. Among them, m is not a positive integer, and 195.1 is rounded to the positive integer 195 according to the rounding principle. According to the duration and sampling rate of the above audio signal, it can be determined that the number of sampling points included in the audio signal is 16000. Then, since the number of sampling points included in the above audio signal is not an integer multiple of 195, after the audio signal is divided into 82 short-time energy frames, the remaining 10 sampling points can be discarded. Among them, the number of sampling points included in each of the above short-time energy frames is 195. When the audio signal obtained in step 101 is an audio signal transmitted from other APPs or devices, any of the above methods can be used to divide the audio signal into multiple short-time energy frames. It should be particularly noted that the format of the above audio signal may not be the PCM format. If the above method is used to divide the short-time energy frame according to the sampling rate of the audio signal and the frequency of the preset speech signal, the received audio signal needs to be converted into an audio signal in the PCM format. In addition, when receiving the audio signal, the sampling rate of the audio signal also needs to be identified. The specific method for identifying the sampling rate of the audio signal can be identified by the methods of the existing technology, which will not be elaborated here one by one. Step 103, determine the energy of each short-time energy frame. In an embodiment of the present application, when the audio signal in PCM format is divided into several short-time energy frames which are also in PCM format by using the above method, the energy of the short-time energy frame can be determined according to the amplitude of the audio signal corresponding to each sampling point in the short-time energy frame. Specifically, the energy of each sampling point can be determined according to the amplitude of the audio signal corresponding to each sampling point in the short-time energy frame, and then these energies are added together, and the final sum of the energies is used as the energy of the short-time energy frame. For example, the following formula can be used to determine the energy of the short-time energy frame: where i represents the i-th sampling point of the audio signal; n is the number of sampling points included in the short-time energy frame; A i [t] is the amplitude of the audio signal corresponding to the i-th sampling point, where the value range of the amplitude of the short-time energy frame is -32768 to 32767. In addition, in an embodiment of the present application, in order to simplify the calculation and save resources, the value obtained by dividing the amplitude obtained when collecting the audio signal by 32768 can be used as the normalized amplitude of the short-time energy frame, and then the value range of the normalized amplitude of the short-time energy frame is -1 to 1. If the format of the short-time energy frame is not in PCM format, the function for calculating the amplitude can be determined according to the amplitude at each moment of the short-time energy frame, and the square of the function is integrated, and the final integration result is the energy of the short-time energy frame. Step 104, detect whether the audio signal contains a voice signal according to the energy of each short-time energy frame. Specifically, the following two methods can be used to determine whether it is detected that the audio signal contains a voice signal: Method 1: Determine the ratio of the number of short-time energy frames with energy greater than the preset threshold to the total number of all short-time energy frames (hereinafter referred to as the high-energy frame ratio), and determine whether the determined high-energy frame ratio is greater than the preset ratio. If so, it is determined that the audio signal contains a voice signal; if not, it is determined that the audio signal does not contain a voice signal. Among them, the sizes of the preset threshold and the preset ratio can be set according to actual needs. In an embodiment of the present application, the preset threshold can be set to 2, and the preset ratio can be set to 20%. If the high-energy frame ratio is greater than 20%, it is determined that the audio signal contains a voice signal; otherwise, it is determined that the audio signal does not contain a voice signal. In the embodiments of the present application, the reason why Method 1 can be used to determine whether a voice signal is included in an audio signal is that in real life, when people talk, there will more or less be some noise in the external environment, and generally the energy of the noise is lower than that of what people say. Then, if there are short-time energy frames with energy higher than a preset threshold in a segment of audio signal, and these short-time energy frames account for a certain ratio in this segment of audio signal, it can be considered that the audio signal contains a voice signal. Method 2: To make the final detection result more accurate, the method mentioned in Method 1 can be used to determine the ratio of high-energy frames, and it is judged whether the determined ratio of high-energy frames is greater than a preset ratio. If not, it is determined that no voice signal is detected in the audio signal; if so, when there are at least N consecutive short-time energy frames among the short-time energy frames with energy greater than the preset threshold, it is determined that a voice signal is detected in the audio signal, and when there are no at least N consecutive short-time energy frames among the short-time energy frames with energy greater than the preset threshold, it is determined that no voice signal is detected in the audio signal. Wherein, N can be any positive integer. In the embodiments of the present application, N can be set to 10. That is to say, on the basis of Method 1, Method 2 adds a condition for determining whether a voice signal is included in the audio signal: whether there are at least N consecutive short-time energy frames among the short-time energy frames with energy greater than the preset threshold. This can effectively reduce noise. Since in real life, the noise has lower energy and is random compared to what humans say, therefore, by using Method 2, the situation of excessive noise in the audio signal can be effectively excluded, and the influence of noise in the external environment can be reduced, achieving the effect of noise reduction. It should be particularly noted that the above-mentioned voice signal detection method provided by the embodiments of the present application is applicable to detecting mono-channel audio signals, stereo audio signals, multi-channel audio signals, etc. Among them, an audio signal collected through one channel is a mono-channel audio signal; an audio signal collected through two channels is a stereo audio signal, and an audio signal collected through multiple channels is a multi-channel audio signal. When using the method shown in Figure 1 to detect stereo audio signals and multi-channel audio signals, the operations mentioned in Steps 101 to 104 can be performed respectively on the audio signals of each obtained channel, and finally, based on the detection results of the audio signals of each channel, it is judged whether the obtained audio signal contains a voice signal. Specifically, if the audio signal obtained in Step 101 is a mono-channel audio signal, the operations mentioned in Steps 101 to 104 can be directly performed on this audio signal, and the detection result is used as the final detection result. If the audio signal obtained in step 101 is not a mono audio signal but a stereo or multi-channel audio signal, then the audio signals of each channel are respectively processed according to the operations in steps 101 to 104. If it is detected that the audio signals of each channel do not contain voice signals, it is determined that the audio signal obtained in step 101 does not contain voice signals. If it is detected that the audio signals of at least one channel contain voice signals, it is determined that the audio signal obtained in step 101 contains voice signals. In addition, the frequency of the preset voice signal mentioned in step 102 can be the frequency of any voice. This application does not make any limitation on this. In practical applications, according to the actual situation, for different audio signals obtained in step 101, different frequencies of the preset voice signal can be set. It should be particularly noted that no matter which voice signal frequency the preset voice frequency is, such as the frequency of a soprano or the frequency of a bass, as long as the short-time energy frames finally divided meet the following conditions: the duration corresponding to the short-time energy frame is not less than the period corresponding to the audio signal obtained in step 101. To achieve a better detection effect, save resources as much as possible, and improve the processing speed, in the embodiments of this application, the frequency of the preset voice signal can be set to the minimum human voice frequency, that is, 82HZ. Since the period is the reciprocal of the frequency, if the frequency of the preset voice signal is the minimum human voice frequency, then the period of the preset voice signal is the maximum human voice period. Therefore, no matter how large the period of the audio signal obtained in step 101 is, the duration corresponding to the short-time energy frame is not less than the period of the above-obtained audio signal. It should be particularly noted that in the embodiments of this application, the reason for making the duration corresponding to the short-time energy frame not less than the period of the audio signal obtained in step 101 is that the detection method provided in the embodiments of this application detects whether the audio signal contains a voice signal based on the characteristics of human speech. Compared with noise, human speech has higher energy, is more stable and continuous. If the duration corresponding to the short-time energy frame is less than the period of the audio signal obtained in step 101, then there is no waveform of a complete period in the waveform corresponding to the short-time energy frame, and the duration of the short-time energy frame is relatively short. In this case, even if the high-energy frame ratio is greater than the preset ratio and there are at least N consecutive short-time energy frames in the short-time energy frames with energy greater than the preset threshold, it only indicates that the audio signal contains a sound signal, but cannot indicate that the sound signal is a voice signal. Therefore, in the embodiments of this application, the duration of the audio signal obtained in step 101 should be greater than the maximum period of a human voice. In addition, the voice signal detection method provided by the embodiments of the present application is particularly applicable to an application scenario where a chat APP can complete the sending of voice messages without any click operation by the user. Then, the voice signal detection method provided by the embodiments of the present application will be described in detail for this scenario. Among them, in this scenario, the specific process schematic diagram of this method is shown in Figure 2 and includes the following steps: Step 201, collect audio signals in real time. If the user hopes that after opening the chat APP, the APP can complete the sending of voice messages without any click operation. Therefore, after the user opens the APP, the APP can start recording the external environment continuously in real time, collect audio signals in real time to avoid missing what the user says as much as possible. In addition, after collecting the audio signals, the audio signals can be saved locally in real time. When the user closes the APP, the APP stops recording. Step 202, intercept an audio signal with a preset duration from the collected audio signals in real time. If the APP keeps recording but does not detect voice signals in real time, it will result in poor timeliness of voice messages. Therefore, the APP can intercept an audio signal with a preset duration from the audio signals collected in Step 201 in real time and perform subsequent detection on the audio signal with the preset duration. Among them, the currently intercepted audio signal with a preset duration can be called the current audio signal, and the previously intercepted audio signal with a preset duration can be called the previously obtained audio signal. Step 203, divide the audio signal with a preset duration into multiple short-time energy frames according to the frequency of the preset voice signal. Step 204, determine the energy of each short-time energy frame. Step 205, detect whether the audio signal with a preset duration contains a voice signal according to the energy of each short-time energy frame. If it is detected that the current audio signal contains a voice signal, then determine whether the previously obtained audio signal contains a voice signal. If it is determined that the previously obtained audio signal does not contain a voice signal, the starting point of the current audio signal can be determined as the starting point of the voice signal; if it is determined that the previously obtained audio signal contains a voice signal, then the starting point of the current audio signal is not the starting point of the voice signal. If it is detected that the current audio signal does not contain a voice signal, then it is judged whether the previously acquired audio signal contains a voice signal. If it is judged that the previously acquired audio signal contains a voice signal, then the end point of the previously acquired audio signal can be determined as the end point of the voice signal; if the previously acquired audio signal does not contain a voice signal, then neither the current audio signal nor the end point of the previously acquired audio signal is the end point of the voice signal. For example, as shown in Figure 3, where A, B, C, and D are four adjacent audio signals of a preset duration, A and D do not contain voice signals, while B and C contain voice signals. Then, the start point of B can be determined as the start point of the voice signal, and the end point of C can be determined as the end point of the voice signal. Sometimes, the current audio signal happens to be the start or end part of a user's sentence, and the voice signal contained in this audio signal is relatively small. In this case, the APP may misjudge this audio signal as not containing a voice signal. Then, in order to avoid missing what the user said due to misjudgment as much as possible, after detecting that the current audio signal contains a voice signal, it can be judged whether the previously acquired audio signal contains a voice signal. If it is judged that the previously acquired audio signal does not contain a voice signal, then the start point of the previously acquired audio signal can be determined as the start point of the voice signal. Additionally, after detecting that the current audio signal does not contain a voice signal, it can be judged whether the previously acquired audio signal contains a voice signal. If it is judged that the previously acquired audio signal contains a voice signal, then the end point of the current audio signal can be determined as the end point of the voice signal. Continuing with the above example, the start point of A can be determined as the start point of the voice signal, and the end point of D can be determined as the end point of the voice signal. After the APP detects that the current audio signal contains a voice signal, it can send this audio signal to a voice recognition device so that the voice recognition device can perform voice processing on this audio signal to obtain a voice result. Then, the voice recognition device sends this audio signal to a subsequent processing device, and finally sends this audio signal in the form of a voice message. Among them, in order to make the words spoken by the user contained in the sent voice message be a complete sentence, after the APP sends all the audio signals between the determined start point and end point of the voice signal to the voice recognition device, it sends an audio termination signal to the voice recognition device to inform the voice recognition device that the current sentence spoken by the user has ended, so that the voice recognition device sends these audio signals to the subsequent processing device together, and finally sends these audio signals in the form of a voice message. In addition, in order to minimize the occurrence of misjudgment, after obtaining the current audio signal, a sub-signal of a preset time period can be intercepted from the previously obtained audio signal, and the current audio signal and the intercepted sub-signal can be spliced together to serve as the obtained audio signal (hereinafter referred to as the spliced audio signal), and subsequent voice signal detection can be performed on the spliced audio signal. Among them, the sub-signal can be spliced before the current audio signal. The preset time period can be the tail time period of the previously obtained audio signal, and the duration corresponding to this time period can be any duration. In order to make the final detection result more accurate, in the embodiments of the present application, the duration corresponding to the preset time period can be set to be not greater than the product of the duration corresponding to the spliced audio signal and the preset ratio. After detecting that the spliced audio signal contains a voice signal, it can be determined whether the previously obtained spliced audio signal contains a voice signal. If it is determined that the previously obtained spliced audio signal does not contain a voice signal, the starting point of the spliced audio signal can be used as the starting point of the voice signal. After detecting that the spliced audio signal does not contain a voice signal, it can be determined whether the previously obtained spliced audio signal contains a voice signal. If it is determined that the previously obtained spliced audio signal contains a voice signal, the end point of the spliced audio signal can be used as the end point of the voice signal. In the embodiments of the present application, in addition to continuously recording, the APP can also record periodically, and the embodiments of the present application do not make any limitations in this regard. The voice signal detection method provided by the embodiments of the present application can also be implemented by a voice signal detection device. The specific structural schematic diagram of this device is shown in Figure 4, and it mainly includes the following devices: An acquisition module 41 that acquires an audio signal; A division module 42 that divides the audio signal into a plurality of short-time energy frames according to the frequency of a preset voice signal; A determination module 43 that determines the energy of each short-time energy frame; A detection module 44 that detects whether the audio signal contains a voice signal according to the energy of each short-time energy frame. In one implementation, the acquisition module 41 acquires the current audio signal; a sub-signal of a preset time period is intercepted from the previously obtained audio signal; The current audio signal and the intercepted sub-signal are spliced together to serve as the obtained audio signal. In one implementation, the division module 42 determines the period of the preset voice signal according to the frequency of the preset voice signal; According to the determined period, the audio signal is divided into a plurality of short-time energy frames with a duration equal to the period. ​In one embodiment, the detection module 44 determines the ratio of the number of short-time energy frames with energy greater than a preset threshold to the total number of all short-time energy frames; Determine whether the ratio is greater than a preset ratio; If so, determine that the voice signal is included in the audio signal is detected; If not, determine that the voice signal is not detected in the audio signal. In one embodiment, the detection module 44 determines the ratio of the number of short-time energy frames with energy greater than a preset threshold to the total number of all short-time energy frames; Determine whether the ratio is greater than a preset ratio; If not, determine that the voice signal is not detected in the audio signal; If so, when there are at least N consecutive short-time energy frames among the short-time energy frames with energy greater than the preset threshold, determine that the voice signal is included in the audio signal is detected, and when there are no at least N consecutive short-time energy frames among the short-time energy frames with energy greater than the preset threshold, determine that the voice signal is not detected in the audio signal. Compared with the prior art detection methods for determining whether a voice signal is included in an audio signal through complex calculations such as Fourier transform, the voice signal detection method adopted in the embodiments of the present application does not require complex calculations such as Fourier transform. By dividing the acquired audio signal into multiple short-time energy frames according to the frequency of the preset voice signal, the energy of each short-time energy frame is determined, and based on the energy of each short-time energy frame, it can be detected whether the acquired audio signal includes a voice signal. Therefore, the voice signal detection method provided by the embodiments of the present application can solve the problems of slow processing speed and high resource consumption existing in the prior art voice signal detection methods. The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in one process or multiple processes and / or one block or multiple blocks in the block diagrams. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in one or more blocks of the flowchart and / or one or more blocks of the block diagram. These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more blocks of the flowchart and / or one or more blocks of the block diagram. In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory. Memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media. Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves. It should also be noted that the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element. Without more constraints, elements defined by the statement "including one..." do not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element. Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

DEPCT6310 / 07 / 25621. Method for measuring acoustic signals, which includes: acoustic signal generation; division of the acoustic signal into a number of short-range energy frames based on the frequency of the specified acoustic signal, which includes human voice; the frequency is set to the lowest human voice frequency; indication of the energy of each short-range energy frame; and measurement, based on the energy of each short-range energy frame, whether the acoustic signal contains acoustic signals.

2. Method based on claim 1, where acoustic signal generation includes: current acoustic signal generation; capture of sub-signals with a defined time period from the final acquired acoustic signal; and joining the current acoustic signal and the captured sub-signals to aid in the acquisition of the acoustic signal.3.The method according to claim 1, where the division of the audio signal into a certain number of short-range energy frames includes: an indication of the range of the audio signal defined based on the frequency of the defined audio signal; and the division of the audio signal based on the indicated range into a certain number of short-range energy frames whose relative time interval is that range.

4. The method according to claim 1, where the measurement based on the energy of each short-range energy frame whether or not the audio signal contains audio signals includes: an indication of the proportion of the quantity of short-range energy frames whose energy exceeds the defined threshold to the total quantity of all short-range energy frames; and an indication of whether the proportion exceeds the defined proportion.

5. The method according to claim 4, where: if it is indicated that the proportion is greater than the defined proportion, it indicates that the audio signal contains audio signals.

6. The method according to claim 4, where: if it is indicated that the proportion is not greater than the defined proportion, it indicates that the audio signal does not contain audio signals. 7.Method according to claim 4, where: if it is indicated that the proportion is not greater than the specified proportion, it indicates that the audio signal is not composed of an audio signal.

8. Method according to claim 4, where: if it is indicated that the proportion is greater than the specified proportion, when there are at least N consecutive short-term energy frames in the short-term energy frame where the energy is greater than the specified threshold, it indicates that the audio signal is composed of an audio signal.

9. Method according to claim 4, where: if it is indicated that the proportion is greater than the specified proportion, when there are not at least N consecutive short-term energy frames in the short-term energy frame where the energy is greater than the specified threshold, it indicates that the audio signal is not composed of an audio signal.

10. Method according to claim 1, which is further incorporated with: indication of the start and end points of the audio signal; adding a stop signal to the audio signal after the end point to indicate that the audio signal is complete; and forwarding the audio signal for further processing. 11.A set of instruments for measuring acoustic signals; an instrument consisting of a number of modules designed to perform one of the methods of claims 1 through 10.-----------------------------------------------------------DEPCT631. An acoustic signal measurement method in which the method includes: acoustic signal generation; division of the acoustic signal into a number of short-range power frames based on the frequency of the specified acoustic signal; indication of the power of each short-range power frame; and measurement based on the power of each short-range power frame whether the acoustic signal contains acoustic signals.

2. A method according to claim 1 in which acoustic signal generation includes: generation of the current acoustic signal; capture of sub-signals with a specified time period from the final acquired acoustic signal; and concatenation of the current acoustic signal and the captured sub-signals to aid in the acquisition of the acoustic signal.3.The method according to claim 1, where the division of the audio signal into a certain number of short-range energy frames based on the specified audio signal frequency, includes: an indication of the range of the specified audio signal based on the specified audio signal frequency; and the division of the audio signal based on the indicated range into a certain number of short-range energy frames, the relative time interval of which is that range.

4. The method according to claim 1, where the measurement based on the energy of each short-range energy frame whether or not the audio signal contains audio or not, includes: an indication of the proportion of the quantity of short-range energy frames whose energy exceeds the specified threshold to the total quantity of all short-range energy frames; an indication of whether the proportion exceeds the specified proportion; and if so, an indication that the audio signal contains audio; or if not, an indication that the audio signal does not contain audio. 5.The method according to claim1, whereby a measurement based on the energy of each short-range energy frame determines whether or not the noise signal is comprised of noise; the indication of the proportion of the quantity of short-range energy frames in which the energy exceeds the specified threshold to the total quantity of all short-range energy frames; the indication of whether the proportion is greater than the specified proportion; and if not, the indication that the noise signal is not comprised of noise; or if so, when there are at least N consecutive short-range energy frames in the short-range energy frame whose energy exceeds the specified threshold, the indication that the noise signal is comprised of noise; or when there are not at least N consecutive short-range energy frames in the short-range energy frame whose energy exceeds the specified threshold, the indication that the noise signal is not comprised of noise.6.The acoustic signal measurement instrument consists of: an acquisition module designed to obtain the acoustic signal; a division module designed to divide the acoustic signal into a number of short-range power frames based on a given acoustic signal frequency; an indication module designed to indicate the power of each short-range power frame; and a measurement module designed to measure, based on the power of each short-range power frame, whether the acoustic signal contains acoustic signals.

7. The instrument under claim 1, where the acquisition module is designed to: obtain the current acoustic signal; capture a sub-signal with a defined time period from the final acquired acoustic signal; and append the current acoustic signal and the captured sub-signal to aid in obtaining the acquired acoustic signal. 8.The instrument under claim 1, whereby the segmentation module is designed to indicate the range of the defined audio signal based on the frequency of the defined audio signal; and to segment the audio signal based on the indicated range into a certain number of short-range energy frames, the relative time being that range.

9. The instrument under claim 1, whereby the measurement module is designed to indicate the proportion of the amount of short-range energy frames in which the energy exceeds a defined threshold to the total amount of all short-range energy frames; to indicate whether the proportion exceeds a defined proportion; and if so, to indicate whether the audio signal is composed of audio signals; or if not, to indicate whether the audio signal is not composed of audio signals.10.The instrument under claim 1, whereby the measurement module is designed to indicate the proportion of the quantity of short-range energy frames in which the energy exceeds a defined threshold to the total quantity of all short-range energy frames; to indicate whether the proportion is greater than the defined proportion; and if not, to indicate that the noise signal is not composed of noise; or if yes, when there are at least N consecutive short-range energy frames in which the energy exceeds the defined threshold, to indicate that the noise signal is composed of noise; or when there are not at least N consecutive short-range energy frames in which the energy exceeds the defined threshold, to indicate that the noise signal is not composed of noise.