Patent Publication Number: US-9411883-B2

Title: Audio signal processing apparatus and method, and monitoring system

Description:
FIELD OF THE INVENTION 
     The invention generally relates to the field of audio processing, and particularly to an audio signal processing apparatus and method and a monitoring system. 
     BACKGROUND OF THE INVENTION 
     An important issue in the field of audio processing is to process an audio signal to accurately recognize sound classes in the audio signal so as to extract a particular audio event. Segmenting the audio signal into successive segments is the basis of the audio signal recognition. The segmenting effect of the audio signal directly affects the accuracy of the audio signal recognition. How to improve the audio signal segmenting technology so as to improve the accuracy of the segmenting and to avoid excessive false segmenting points, large computing capacity, high false detection rate and missing detection rate is an important aspect of research in this field. 
     SUMMARY OF THE INVENTION 
     A brief overview of the invention is given hereinafter in order to provide basic understanding regarding some aspects of the invention. It should be understood that this overview is not an exhaustive overview of the invention. It is neither intended to determine the key or critical part of the invention, nor intended to limit the scope of the invention. Its purpose is to merely give some concepts in a simplified form as a prelude to the more detailed description that is discussed later. 
     The object of the invention is to provide an audio signal processing apparatus and method and a monitoring system to overcome at least one of the above problems in the prior art. 
     According to an aspect of the invention, there is provided an audio signal processing apparatus including: a window dividing unit for sequentially reading an inputted audio signal using a sliding window; an energy calculating unit for calculating energy of each frame of the audio signal in each window; a segmenting unit for segmenting, according to distribution of the energy of each frame of the audio signal in each window, the audio signal in the window into multiple segments, such that each segment includes successive frames with approximate energies; a classifying unit for classifying the audio signal in each segment using at least one sound model; and a recognizing unit for recognizing a sound class of the audio signal in each segment according to a result of the classifying by the classifying unit. 
     According to another aspect of the invention, there is provided an audio signal processing method, including: sequentially reading an inputted audio signal using a sliding window; calculating energy of each frame of the audio signal in each window; segmenting, according to distribution of the energy of the each frame of the audio signal in each window, the audio signal in the window into multiple segments, such that each segment includes successive frames with approximate energies; classifying the audio signal in each segment using at least one sound model; and recognizing a sound class of the audio signal in each segment according to a result of the classifying. 
     According to still another aspect of the invention, there is provided a monitoring system including: an audio collecting apparatus for collecting an audio signal; an audio signal processing apparatus for processing the audio signal, so as to recognize sound classes included in the audio signal; and an alerting apparatus for generating and transmitting alert information when the audio signal processing apparatus recognizes that the audio signal includes a sound class of a predetermined type, wherein the audio signal processing apparatus is the audio signal processing apparatus according to the above aspects of the invention. 
     In the audio signal processing apparatus and method and the monitoring system according to the above aspects of the invention, the audio signal in each window is segmented into multiple segments according to distribution of the energy of each frame of the audio signal in the window such that each segment includes successive frames with approximate energies, and the audio signal in each segment is classified. In this way, the audio signal can be front-end segmented rapidly, without previously training a sound model for the segmenting, and since each segment includes successive frames with approximate energies, the sound class included in each segment is relatively single, thereby facilitating the improvement of accuracy of the subsequent audio signal recognition. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention can be better understood from the description given hereinafter in conjunction with drawings, and the same or similar parts are denoted by the same or similar reference numerals throughout the drawings. The drawings together with the following detailed description are contained in the specification as a part of the specification, and are used to further illustrate the preferred embodiments of the invention and explain the principles and advantages of the invention. In the drawings: 
         FIG. 1  shows a schematic block diagram of an audio signal processing apparatus according to an embodiment of the invention; 
         FIG. 2  shows a schematic operation flowchart of the audio signal processing apparatus shown in  FIG. 1 ; 
         FIG. 3  shows a schematic block diagram of a segmenting unit according to an embodiment of the invention; 
         FIG. 4  shows a schematic operation flowchart of the segmenting unit shown in  FIG. 3 ; 
         FIG. 5  shows a schematic flowchart of an example of a segmenting process according to an embodiment of the invention; 
         FIG. 6  shows a schematic block diagram of a segmenting unit according to another embodiment of the invention; 
         FIG. 7  shows a schematic block diagram of an audio signal processing apparatus according to another embodiment of the invention; 
         FIG. 8  shows a schematic operation flowchart of the audio signal processing apparatus shown in  FIG. 7 ; 
         FIG. 9  shows a schematic block diagram of an audio signal processing apparatus according to still another embodiment of the invention; 
         FIG. 10  shows a schematic operation flowchart of the audio signal processing apparatus shown in  FIG. 9 ; 
         FIG. 11  shows a schematic block diagram of a monitoring system according to an embodiment of the invention; and 
         FIG. 12  shows an exemplary block diagram of the structure of a computer which can implement the embodiments/examples of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The embodiments of the invention will be described hereinafter with reference to the drawings. Elements and features described in one drawing or embodiment of the invention can be combined with elements and features described in another drawing or embodiment of the invention. It should be noted that, for purposes of clarity, representations and descriptions on components and processes that are unrelated to the invention and that have been known to those skilled in the art are omitted in the drawings and descriptions. 
       FIG. 1  shows a schematic block diagram of an audio signal processing apparatus according to an embodiment of the invention. As shown in  FIG. 1 , an audio signal processing apparatus  100  includes a window dividing unit  110 , an energy calculating unit  120 , a segmenting unit  130 , a classifying unit  140  and a recognizing unit  150 . A schematic operation flowchart of the audio signal processing apparatus  100  may be described below in conjunction with  FIG. 2 . 
       FIG. 2  shows a schematic operation flowchart of the audio signal processing apparatus  100  shown in  FIG. 1 , i.e. an audio signal processing method according to an embodiment of the invention. As shown in  FIG. 2 , in a method P200, in step S 210 , an inputted audio signal is sequentially read using a sliding window. By taking each window of signal as a processing unit of subsequent operations such as segmenting, classifying and recognizing, each window of the audio signal is sequentially processed. In step S 220 , energy of each frame of the audio signal in each window is calculated. Frame is a basic unit of the audio signal, and each frame of the audio signal has a predetermined time length. In step S 230 , the audio signal in each window is segmented into multiple segments according to distribution of energy of each frame of the audio signal in the window, such that each segment includes successive frames with approximate energies. That is to say, the change of energies of frames in each segment is relatively flat. In step S 240 , the audio signal in each segment is classified using at least one sound model. The sound model may be a pre-trained model, and each sound model corresponds to a respective sound class. The degree of similarity (e.g., the likelihood value or score) of the audio signal in each segment with respect to the at least one sound model is determined by classifying, to serve as a result of the classifying. In this embodiment, there is no limitation to the employed specific classification method. For example, the sound model may be used to classify a characteristic parameter of the entire audio signal of each segment, so as to obtain a classification result of this segment; alternatively, the sound model may be used to classify a characteristic parameter of each frame of the audio signal in each segment, and the classification result of this segment is be determined according to the classification results of respective frames in the segment. In step S 250 , the sound class of the audio signal in each segment is recognized according to the classification result. Specifically, if the audio signal has a higher degree of similarity with respect to a certain sound model, then it is determined that the audio signal belongs to a sound class corresponding to the certain sound model. Here, step S 210  may be performed by the window dividing unit  110 , step S 220  may be performed by the energy calculating unit  120 , step S 230  may be performed by the segmenting unit  130 , step S 240  may be performed by the classifying unit  240 , and step S 250  may be performed by the recognizing unit  150 . 
     Thus, the audio signal can be front-end segmented rapidly, without previously training a sound model for the segmenting. Successive frame having relatively approximate energies can be considered to have the same class, successive frames having larger energy differences therebetween are considered to have different classes. Since each segment includes successive frames with approximate energies, the sound class included in each segment is relatively single, thereby facilitating the improvement of accuracy of the subsequent audio signal recognition. 
     It should be understood that in the method P200, the timing for performing the energy calculation step S 220  is not limited to that shown in  FIG. 2 , but the calculation for energy of each frame in the audio signal may also be performed before the window dividing step S 210 . 
     The window dividing unit  110  may use any appropriate existing techniques or any appropriate techniques to be developed to move the sliding window on the inputted audio signal (the window dividing operation). For example, a sliding window with a predetermined fixed-length may be used to read the audio signal. Alternatively, a sliding window with a variable length may be used to read the audio signal. 
     As an example of a sliding window with a variable length, the window dividing unit  110  can take a predetermined number of frames as the length of an initial sliding window, slide the sliding window by increasing a fixed step length (fixed number of frames) each time and taking the back boundary of the initial sliding window as a center, to look for the minimum extreme points of an energy envelope of the audio signal as the front boundary of the next window. In order to prevent false judgments caused by disturbance, the minimum extreme point does not include extreme points generated by slight disturbance. Here, the one in the two boundaries of the sliding window which is at the front in time is called a front boundary, and the one in the two boundaries of the sliding window which is at the back in time is called a back boundary. 
     The energy calculating unit  120  may use various appropriate methods to calculate the energy of each frame of the audio signal. For example, the following equation may be used to calculate the energy of one frame of the audio signal: 
     
       
         
           
             
               
                 
                   
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     In the above equation, i is the frame number of the current frame; N is the size of a frame shift, i.e. the number of sampling points contained in an overlapping portion of adjacent frames; M is the total number of sampling points in one frame, which depends on a frame length and an encoding rate of the audio signal; j represents the number of a sampling point in a frame, which is also known as a local number; and S (i*N+j)  represents the amplitude of a sample point in the audio signal which has a global number (i*N+j). 
     In order to rapidly and accurately segment the audio signal, the segmenting unit  130  segments the audio signal in each window into segments each of which includes successive frames with approximate energies according to distribution of energy of each frame of the audio signal in the window. 
       FIG. 3  shows a schematic block diagram of a segmenting unit according to an embodiment of the invention. In  FIG. 3 , the segmenting unit  130  includes a clustering unit  131  and a segment forming unit  132 .  FIG. 4  shows a schematic operation flowchart of the segmenting unit shown in  FIG. 3 . Specifically, step S 230  includes: a step S 231  which may be performed by the clustering unit  131  for clustering energies of frames in each window into multiple clusters according to magnitudes of the energies, each cluster containing multiple energies whose magnitudes are approximate to each other; and a step S 232  which may be performed by the segment forming unit  132  for forming successive frames in the same cluster into one segment. 
     In a clustering example, the clustering unit  131  clusters an energy sequence of the window into two clusters according to a Nearest Neighbor Rule by taking a maximum energy and a minimum energy in the energy sequence of the window as centers, respectively, and iteratively clusters an energy sequence of each cluster according to the Nearest Neighbor Rule by taking a maximum energy and a minimum energy in the energy sequence of each cluster as centers, respectively, until a clustering condition is not satisfied any longer. If the two clusters resultant from the current clustering do not satisfy the clustering condition, the current clustering is withdrawn. The clustering condition is that the sum of degrees of similarity between distributions of the energy sequences of the two clusters resultant from the clustering and Single Gaussian Distribution is higher by up to a predetermined extent than a degree of similarity between distribution of an energy sequence of a window or cluster from which the two clusters are clustered and the Single Gaussian Distribution. Alternatively, the clustering condition is that the sum of the extents that the energy sequences of the two clusters resultant from the clustering follow Single Gaussian Distribution is higher by up to a predetermined extent than the extent that the energy sequence of the window or cluster from which the two clusters are clustered follows the Single Gaussian Distribution. The sequence composed of energies of frames in each window may be called energy sequence of this window, and the sequence composed of energies of frames in each cluster may be called energy sequence of this cluster. The Nearest Neighbor rule means that an energy in the energy sequence is classified into a cluster which takes one of the maximum energy and the minimum energy that the magnitude of the energy is more close to, as a center. 
     When the degrees of similarity between distributions of energy sequences of two clusters resultant from the clustering and the Single Gaussian Distribution are higher than the degree of similarity between distribution of the window or cluster from which the two clusters are clustered and the Single Gaussian Distribution, it indicates that the approximation degree between energies of the frames in the two clusters resultant from this clustering is higher than the approximation degree between energies of the frames in the window or cluster from which the two clusters are clustered, and the sound class included in a segment formed from the two clusters will be more single. 
     It should be understood that, when the clustering is performed on the energy sequence, it is not limited to take the maximum energy and the minimum energy as energy centers, and more energy centers with different magnitudes may also be set finely to perform clustering. Accordingly, the clustering condition may also be adjusted to that the sum of the degrees of similarity between distributions of energy sequences of the multiple clusters resultant from the clustering and the Single Gaussian Distribution is higher by up to a predetermined extent than the degree of similarity between distribution of energy sequences of the window or cluster from which the multiple clusters are clustered and the Single Gaussian Distribution. Further, for the distribution of the energy sequence, it is not limited to perform fitting using the single Gaussian distribution, other distributions similar to the Single Gaussian Distribution may also be used to perform fitting. 
     An example of a segmenting process according to an embodiment of the invention is described hereinafter in conjunction with  FIG. 5 . In the example of  FIG. 5 , an audio signal in one window is segmented. 
     As shown in  FIG. 5 , in a method P500, the segmenting process is started by taking the energy sequence in the window as the current energy sequence. In step S 510 , a maximum energy and a minimum energy in the current energy sequence and the mean and variance of the current energy sequence are calculated. 
     In step S 520 , a Gaussian distribution probability density function is constructed according to the mean and variance of the current energy sequence, and a degree of similarity Lp between distribution of the current energy sequence and the Single Gaussian Distribution is calculated. More specifically, the Gaussian distribution probability density function may be constructed by using the mean and variance of the current energy sequence as a mathematical expectation and variance, and the Gaussian distribution probability density function represents a Single Gaussian Distribution corresponding to the current energy sequence. The probability of each element in the current energy sequence is calculated using this Gaussian distribution probability density function, and the sum of the probabilities of all elements in the current energy sequence is used as a degree of similarity Lp between the distribution of the current energy sequence and the Single Gaussian Distribution. 
     In step S 530 , the current energy sequence is clustered into two clusters c1 and c2 (two classes) according to a Nearest Neighbor Rule by taking a maximum energy and a minimum energy in the current energy sequence as centers. 
     In step S 540 , the mean and variance of the energy sequence of each cluster resultant from the clustering are calculated. 
     In step S 550 , the Gaussian distribution probability density function is constructed according to the mean and variance of the energy sequence of each cluster, and degrees of similarity Lc1 and Lc2 between distributions of the energy sequences of the two clusters c1 and c2 and the Single Gaussian Distribution are calculated respectively. More specifically, for each cluster c1 or c2, the Gaussian distribution probability density function may be constructed by taking the mean and variance of the energy sequence of the cluster as a mathematical expectation and variance, and this Gaussian distribution probability density function represents a Single Gaussian Distribution corresponding to the energy sequence of this cluster. The probability of each element in the energy sequence of the cluster is calculated using this Gaussian distribution probability density function, and the sum of probabilities of all elements in the energy sequence is used as a degree of similarity between the distribution of the energy sequence of this cluster and the Single Gaussian Distribution. 
     In step S 560 , it is judged whether a difference between the sum of degrees of similarity Lc1 and Lc2 and the degree of similarity Lp is greater than or equal to a predetermined threshold Lth. 
     If ((Lc1+Lc2)−Lp)≧Lth, it indicates that this clustering satisfies the clustering condition, and the process proceeds to S 510  to perform the next level of clustering by taking the energy sequence of each cluster resultant from the clustering as the current energy sequence, respectively. 
     Otherwise, it indicates that this clustering does not satisfy the clustering condition, and the process proceeds to step S 570  to withdraw this clustering. Then, in step S 580 , for the individual clusters that have been generated from valid clustering, successive frames in the same cluster are formed into one segment, thereby segmenting the audio signal in the window into multiple segments. 
     The above example is merely for the purpose of illustration and not for limitation. For example, as another example of a method for determining a degree of similarity between distribution of the energy sequence and the Single Gaussian Distribution, the mean of the energy sequence can be firstly calculated, and a distribution curve of energy values in the energy sequence is plotted by taking the mean of the energy sequence as a center. The distribution curve is compared in shape with any appropriate Single Gaussian Distribution curve such as a standard Gaussian distribution curve, and the degree of similarity between the distribution of the energy sequence and the Single Gaussian Distribution is determined according to a degree of similarity in shape. From the above examples, those skilled in the art can consider more methods for determining the degree of similarity between the distribution of the energy sequence and the Single Gaussian Distribution, which will not be described herein in detail. 
     According to an embodiment of the invention, before the clustering unit  131  performs clustering, a sequence composed of energies of frames in a window can be regulated to increase differences between energies in the sequence.  FIG. 6  shows a schematic block diagram of a segmenting unit according to this embodiment. Compared with the segmenting unit  130  shown in  FIG. 3 , the segmenting unit  130 A in  FIG. 6  further includes an energy regulating unit  133  for performing the regulating operation described above. The energy regulating unit  133  can perform energy regulation using any existing appropriate method. As an example, the energy regulating unit  133  may use the following equation to regulate the energy sequence of one window: 
     
       
         
           
             
               
                 
                   
                     
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     where i is the index of a frame, i.e. a frame number; E max  is a maximum energy in the energy sequence; E min  is a minimum energy in the energy sequence; E(i) is the energy of a frame i before regulation; Ê(i) is the energy of a frame i after regulation; σ is a scale parameter and is an empirical value. 
     In practical applications, abnormal sound detection or recognition is an important application of the audio signal recognition. For example, in some unattended environments, a monitoring device is required to detect or recognize abnormal sounds such as gunshots, screams, sound of broken glass, and to transmit alert information. However, since the acoustic characteristic of the abnormal sound is often similar to the ambient noise, it is prone to generate false alerts or missing detection. In order to reduce the false alerting rate and the missing detection rate in the abnormal sound detection, in another embodiment of the invention, the classification result by the classifying unit  140  is weighted. 
       FIG. 7  shows a schematic block diagram of an audio signal processing apparatus according to another embodiment of the invention  7 . As shown in  FIG. 7 , in addition to the window dividing unit  110 , the energy calculating unit  120 , the segmenting unit  130 , the classifying unit  140  and the recognizing unit  150  shown in  FIG. 1 , the audio signal processing apparatus  100 A further includes a weighting unit  160 . 
     Here, more specifically, the classifying unit  140  uses an abnormal sound model and a background model to classify each frame of the audio signal in each segment. The weighting unit  160  performs weighting on the classification result for each frame by the classifying unit  140  according to a reliability that each frame belongs to abnormal sound. If the reliability is larger, then the weight of the classification result is greater. As an example, the reliability that each frame belongs to abnormal sound may be directly used as the weight of the classification result of this frame. Accordingly, the recognizing unit  150  recognizes a sound class of the audio signal in each segment according to the weighted classification result of each frame.  FIG. 8  shows a schematic operation flowchart of the audio signal processing apparatus shown in  FIG. 7 , i.e., an audio signal processing method according to another embodiment of the invention. In this method P200A, step S 260  describes the function performed by the weighting unit  160  described above. The step S 260  may be performed in parallel with the segmenting step S 230  and the classifying step S 240  as shown in the figure, and may also be performed before the step S 230 , S 240  or S 250 . 
     The analysis shows that, in terms of energy, if the change in energies of adjacent frames is larger, then the possibility that abnormal sound occurs is greater. Thus, in one embodiment of the invention, the weighting unit  160  uses the energy change of each frame of the audio signal with respect to a previous frame of the audio signal as the reliability that each frame belongs to the abnormal sound. For example, the weighting unit  160  may use the following equation to calculate the reliability that each frame belongs to abnormal sound, and use the reliability as the weight of the classification result of this frame:
 
 w ( i )=| E ( i )− E ( i− 1)|/ E ( i− 1)  Equation (3)
 
     In this equation, i is the index of a frame in a segment, i.e. a frame number; E (i) is the energy of a frame i; E (i−1) is the energy of a previous frame i−1 of the frame i. For the start frame in the segment, the energy of its previous frame may be the energy of the last frame in the previous adjacent segment. 
     By analyzing the degree of similarity of the characteristics of signals of each frame with respect to the abnormal sound model and the background sound model, it is found that most false alerts occur in a situation that the degree of similarity with respect to the abnormal sound model is very close to the degree of similarity with respect to the background sound model. That is to say, if the two degrees of similarity are very close, the possibility that the false alert occurs is greater, and the sound model has less distinction; on the contrary, if the difference between the two degrees of similarity is larger, the possibility that the false alert occurs is less, and the sound model has a stronger distinction. Thus, in one embodiment of the invention, the weighting unit  160  may use a difference between the degree of similarity between each frame of the audio signal and the abnormal sound model and the degree of similarity between each frame of the audio signal and the background sound model as the reliability that each frame belongs to the abnormal sound. Specifically, the degree of similarity between each frame of the audio signal and the abnormal sound model refers to a degree of similarity between a feature of each frame of the audio signal and the abnormal sound model, and the degree of similarity between each frame of the audio signal and the background sound model refers to a degree of similarity between a feature of each frame of the audio signal and the background sound model. The feature of the audio signal is not defined herein, and any appropriate feature of the audio signal and corresponding abnormal sound model and background sound model may be used. For example, the weighting unit  160  may use the following equation to calculate the reliability that each frame belongs to the abnormal sound, and use the reliability as the weight of the classification result of this frame: 
     
       
         
           
             
               
                 
                   
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     where i is the index of a frame in a segment, i.e. a frame number; L T (i) represents a degree of similarity between a feature of a frame i and an abnormal sound model T, and L BG (i) represents a degree of similarity between a feature of a frame i and a background sound model BG. 
     Further, it is also found by analysis that if the number of consecutive frames contained in one segment is less, the possibility that the frames belong to burst noises is larger and the possibility that the frames belong to the abnormal sound is less. On the contrary, if the continuity of frames in a segment is stronger, i.e. the number of successive frames contained in this segment is larger, then the possibility that the frames belong to abnormal sound is larger. Thus, in one embodiment of the invention, the weighting unit  160  may use the number of successive frames contained in the segment where each frame lies as the reliability that each frame belongs to the abnormal sound. Accordingly, the weighting unit  160  may set the weight of the classification result of each frame to be a value corresponding to the number of successive frames contained in the segment where the frame belongs to. For example, if the number of successive frames in a segment is 1, then the weight of frames in the segment is determined to be −0.2; if the number of successive frames in a segment is 2, then the weight of frames in the segment is determined to be −0.1; if the number of successive frames in a segment is 3, then the weight of frames in the segment is determined to be 1; and if the number of successive frames in a segment is greater than 3, then the weight of frames in the segment is determined to be 1+0.1*L, where L is the number of successive frames in the segment. 
     The reliabilities that each frame belongs to the abnormal sound in the embodiment described above may be used in combination. For example, the weighting unit  160  may use a combination of the energy change of each frame of the audio signal with respect to the previous frame of the audio signal and a difference between the degree of similarity between each frame of the audio signal and the abnormal sound model and the degree of similarity between each frame of the audio signal and the background sound model as the reliability that each frame belongs to the abnormal sound. As an example, the weighting unit  160  may use the following equation to calculate the reliability that each frame belongs to the abnormal sound and uses the reliability as the weight of the classification result of this frame: 
     
       
         
           
             
               
                 
                   
                     w 
                     ⁡ 
                     
                       ( 
                       i 
                       ) 
                     
                   
                   = 
                   
                     { 
                     
                       
                         
                           
                             
                               delta_E 
                               ⁢ 
                               
                                 ( 
                                 i 
                                 ) 
                               
                               * 
                               
                                 ( 
                                 
                                   
                                     - 
                                     delta_L 
                                   
                                   ⁢ 
                                   
                                     ( 
                                     i 
                                     ) 
                                   
                                 
                                 ) 
                               
                             
                             , 
                           
                         
                         
                           
                             
                               
                                 if 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   θ 
                                   2 
                                 
                               
                               &lt; 
                               
                                 delta_L 
                                 ⁢ 
                                 
                                   ( 
                                   i 
                                   ) 
                                 
                               
                               &lt; 
                               
                                 θ 
                                 1 
                               
                             
                             , 
                           
                         
                       
                       
                         
                           
                             
                               delta_E 
                               ⁢ 
                               
                                 ( 
                                 i 
                                 ) 
                               
                               * 
                               
                                  
                                 
                                   delta_L 
                                   ⁢ 
                                   
                                     ( 
                                     i 
                                     ) 
                                   
                                 
                                  
                               
                             
                             , 
                           
                         
                         
                           
                             
                               
                                 if 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 delta_L 
                                 ⁢ 
                                 
                                   ( 
                                   i 
                                   ) 
                                 
                               
                               &lt;= 
                               
                                 θ 
                                 2 
                               
                             
                             , 
                           
                         
                       
                       
                         
                           
                             
                               delta_E 
                               ⁢ 
                               
                                 ( 
                                 i 
                                 ) 
                               
                             
                             , 
                           
                         
                         
                           
                             
                               if 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               delta_L 
                               ⁢ 
                               
                                 ( 
                                 i 
                                 ) 
                               
                             
                             &lt;= 
                             
                               θ 
                               1 
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     6 
                   
                   ) 
                 
               
             
           
         
       
     
     where i is the index of a frame in a segment, i.e. a frame number; delta_E (i) represents the energy change of a frame i with respect to an adjacent frame, and delta_E (i)=|E (i)−E(i−1)|/E(i−1); E(i) and E(i−1) respectively represent the energy of a frame i and the energy of a previous frame i−1; delta_L (i) represents a difference between a degree of similarity L T  (i) of a frame i with respect to an abnormal sound model T and a degree of similarity L BG  (i) of the frame i with respect to a background sound model BG, and delta_L (i)=LBG (i)−LT (i); and θ 1  and θ 2  are two predetermined thresholds which are empirical values, and in this example, which may be set as for example θ 1 =1, θ 2 =−5. 
     Accordingly, the recognizing unit  150  recognizes the sound class of the audio signal in each segment according to the weighted classification result of each frame. For example, it is assumed that there are three abnormal sound models (T1, T2, T3) and one background sound model (BG). For the abnormal sound model T1, the recognizing unit  150  may use the following equation to calculate the weighted degree of similarity of the segment with respect to the sound model T1: 
     
       
         
           
             
               
                 
                   
                     
                       L 
                       _ 
                     
                     
                       T 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       1 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         0 
                       
                       M 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         w 
                         ⁡ 
                         
                           ( 
                           i 
                           ) 
                         
                       
                       * 
                       
                         
                           L 
                           
                             T 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             1 
                           
                         
                         ⁡ 
                         
                           ( 
                           i 
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     7 
                   
                   ) 
                 
               
             
           
         
       
     
     where i is the index of a frame in a segment, i.e., a frame number; L T1 (i) represents a degree of similarity between a feature of a frame i and an abnormal sound model T1, i.e., a classification result of a frame i; w(i) represents the weight of the classification result of a frame i; and M represents the total number of frames contained in the segment. 
     Similarly, the weighted degrees of similarity  L   T2 ,  L   T3 ,  L   BG  of the segment with respect to the sound models T2, T3 and BG may be calculated. Then, the recognizing unit  150  compares the degrees of similarity  L   T1 ,  L   T2 ,  L   T3 , {circumflex over (L)} BG , and determines the sound class which is represented by the sound model corresponding to the maximum weighted degree of similarity as the sound class of the audio signal in this segment. 
     In the above example, the recognizing unit  150  recognizes the sound class of the audio signal in the segment by calculating the sum of the weighted classification results of individual frames in the segment. This example is for the purpose of illustration and not for limitation, and the weighted classification results of individual frames may also be used in other ways. For example, in another example, the recognizing unit  150  may recognize the sound class of the audio signal in the segment by calculating the weighted mean of the classification result of each frame in the segment. That is, the weighted degree of similarity of the segment with respect to for example the sound model T1 can be modified as: 
     
       
         
           
             
               
                 
                   
                     
                       L 
                       _ 
                     
                     
                       T 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       1 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           0 
                         
                         M 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         
                           w 
                           ⁡ 
                           
                             ( 
                             i 
                             ) 
                           
                         
                         * 
                         
                           
                             L 
                             
                               T 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               1 
                             
                           
                           ⁡ 
                           
                             ( 
                             i 
                             ) 
                           
                         
                       
                     
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           0 
                         
                         M 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         w 
                         ⁡ 
                         
                           ( 
                           i 
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     8 
                   
                   ) 
                 
               
             
           
         
       
     
     where i is the index of a frame in a segment, i.e., a frame number; L T1 (i) represents a degree of similarity between a feature of a frame i and an abnormal sound model T1, i.e., a classification result of the frame i; w (i) represents the weight of the classification result of the frame i; and M represents the total number of frames contained in the segment. 
     Similar to the equation 8, the weighted degrees of similarity  L   T2 ,  L   T3 ,  L   BG  of the segment with respect to the sound models T2, T3 and BG may be calculated. Then, the recognizing unit  150  compares the degrees of similarity  L   T1 ,  L   T2 ,  L   T3  and  L   BG , and determines the sound class which is represented by the sound model corresponding to the maximum weighed degree of similarity as the sound class of the audio signal in this segment. 
     One sound class is detected for each segment, which may reduce the disturbance introduced by signal mutation. Further, since the distinction between weighted classification results is large, the accuracy of the audio signal recognition is improved and the false alerts and missing detection situations are reduced. 
       FIG. 9  shows a schematic block diagram of an audio signal processing apparatus according to still another embodiment of the invention. In this embodiment, in addition to the window dividing unit  110 , the energy calculating unit  120 , the segmenting unit  130 , the classifying unit  140  and the recognizing unit  150  shown in  FIG. 1 , the audio signal processing apparatus  100 B further includes an energy smoothing unit  170  for smoothing the energy of each frame of the audio signal in a sliding window before segmenting the audio signal in the sliding window, to eliminate the effects of energy mutation points to the segmenting. The audio signal processing apparatus  100 B may also include a weighting unit  160  (not shown) to perform the function of the weighting unit  160  shown in  FIG. 7 , and the description thereof is omitted here. 
     As an example, the energy smoothing unit  170  may use the following equation to smooth the energy: 
     
       
         
           
             
               
                 
                   
                     
                       E 
                       _ 
                     
                     ⁡ 
                     
                       ( 
                       i 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         
                           2 
                           ⁢ 
                           K 
                         
                         + 
                         1 
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           0 
                         
                         
                           k 
                           = 
                           K 
                         
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         E 
                         ⁡ 
                         
                           ( 
                           
                             i 
                             + 
                             k 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     9 
                   
                   ) 
                 
               
             
           
         
       
     
     where i is the index of a frame i, i.e. a frame number; k is the index of the frame i in a smoothing window; and K is the width of the smoothing window. 
       FIG. 10  shows a schematic operation flowchart of the audio signal processing apparatus shown in  FIG. 9 , i.e., an audio signal processing method according to still another embodiment of the invention. In this method P200B, the step S 270  describes the function performed by the energy smoothing unit  170  described above. 
     The audio signal processing apparatus and method according to the embodiment described above of the invention may be applied to a monitoring system.  FIG. 11  shows a schematic block diagram of a monitoring system according to an embodiment of the invention. As shown in  FIG. 11 , the monitoring system  1100  includes an audio signal collecting apparatus  1110 , an audio signal processing apparatus  1120  and an alerting apparatus  1130 . The audio signal collecting apparatus  1110  is configured to collect an audio signal. The audio signal processing apparatus  1120  is configured to process the audio signal to recognize sound classes contained in the audio signal. The alerting apparatus  1130  is configured to generate and transmit alert information when the audio signal processing apparatus  1120  recognizes that the audio signal includes a sound class of a predetermined type. Specifically, the audio signal processing apparatus  1120  may be implemented by any one of the audio signal processing apparatuses  100 ,  100 A and  100 B according to the embodiments of the invention described above. The audio signal collecting apparatus  1110  and the alerting apparatus  1130  can be configured using any existing technique or any appropriate technique to be developed, which is not described here in detail to avoid unnecessarily obscuring the scope of the invention. 
     It should be understood that the various component parts and units in the apparatuses of the embodiments of the invention may be configured in a way of software, firmware, hardware, or a combination thereof. Specific means or methods used in the configuring are well known to those skilled in the art and thus are not discussed here in detail. In the case of software or firmware, programs constituting the software are installed to a computer with a dedicated hardware structure from a storage medium or a network, and the computer can execute various functions when being installed with various programs. 
       FIG. 12  shows an exemplary block diagram of the structure of a computer  1200  for implementing the embodiments/examples of the invention. In  FIG. 12 , a central processing unit (CPU)  1201  executes various processes according to programs stored in a read only memory (ROM)  1202  or programs loaded to a random access memory (RAM)  1203  from a storage section  1208 . The RAM  1203  also stores data required when the CPU  1201  executes various processes and the like, if necessary. CPU  1201 , ROM  1202  and RAM  1203  are connected to each other via a bus  1204 . An input/output interface  1205  is also connected to the bus  1204 . 
     The following components are connected to the input/output interface  1205 : an input section  1206  (including a keyboard, a mouse, etc.), an output section  1207  (including a display such as a cathode ray tube (CRT) display and a liquid crystal display (LCD), and a speaker, etc.), a storage section  1208  (including a hard disk, etc.), a communication section  1209  (including a network interface card such as a LAN card, a modem). The communication section  1209  performs a communication process via a network such as Internet. According to needs, a driver  1210  may also be connected to the input/output interface  1205 . A removable medium such as a disk, an optical disk, a magneto-optical disk or a semiconductor memory may be mounted on the driver  1210  as required, so that computer programs read therefrom are installed to the storage section  1208  as required. 
     In case of realizing the above-described series of processes by software, programs constituting the software are installed from a network such as Internet or from a storage medium such as the removable medium  1211 . 
     Those skilled in the art should understand that such storage medium is not limited to the removable medium  1211  shown in  FIG. 12  which stores thereon programs and is distributed separately from a device to provide programs to a user. Examples of the removable medium  1211  include disk (including a floppy disk (registered trademark)), CD (including compact disc read-only memory (CD-ROM) and digital versatile disc (DVD)), magneto-optical disk (including mini disc (MD) (registered trademark)) and a semiconductor memory. Alternatively, the storage medium may be the ROM  1202 , or a hard disk contained in the storage section  1208 , etc., which stores thereon programs and is distributed to the user together with a device containing it. 
     The invention also provides a program product which stores thereon machine-readable instruction codes. When the instruction codes are read and executed by a machine, the audio signal processing method described above according to the embodiments of the invention can be performed. 
     Accordingly, the storage medium for carrying the program product which stores thereon machine-readable instruction codes described above is also included in the invention. The storage medium includes but not limited to floppy diskettes, optical disks, magneto-optical disks, memory cards, memory sticks and so on. 
     In the foregoing description of the embodiments of the invention, features described and/or shown for one embodiment may be used in one or more other embodiments in a same or similar way, may be used in combination with features in other embodiments, or may replace features in other embodiments. 
     It should be emphasized that the term “include/comprise”, when used herein, refers to the presence of features, elements, steps or components but does not preclude the presence or addition of one or more other features, elements, steps or components. 
     Furthermore, the method according to the invention is not limited to be executed according to the time order described in the specification, and can also be executed sequentially, in parallel or independently in accordance with another time order. Therefore, the order of execution of the method described in the specification does not constitute limitations to the technical scope of the invention. 
     While the embodiments of the invention are described above in detail in conjunction with drawings, it should be understood that the embodiments described above are merely for illustrating the invention and are not intended to limit the invention. For those skilled in the art, various modifications and alterations can be made to the embodiments described above without departing from the spirit and scope of the invention. Therefore, the scope of the invention is defined by only the appended claims and equivalent meanings thereof.