Abstract:
A system is described that constantly learns the sound characteristics of an indoor environment to detect the presence or absence of humans within that environment. A detection model is constructed and a decision feedback approach is used to constantly learn and update the statistics of the detection features and sound events that are unique to the environment in question. The learning process may not only rely on acoustic signal, but may also make use of signals derived from other sensors such as range sensor, motion sensors, pressure sensors, and video sensors.

Description:
RELATED APPLICATIONS 
       [0001]    This application claims priority to U.S. Provisional Patent Application No. 62/018,665, filed Jun. 30, 2014, entitled LEARNING ALGORITHM TO DETECT HUMAN PRESENCE IN INDOOR ENVIRONMENTS FROM ACOUSTIC SIGNALS, the contents of which are incorporated by reference herein in their entirety for all purposes. 
     
    
     BACKGROUND 
       [0002]    This invention generally relates to the detection of human presence in an indoor environment. More particularly, embodiments of this invention relate to the detection of human presence or absence in an indoor environment using acoustic signals. 
         [0003]    Detection of the presence or absence of humans in an indoor environment using acoustic signals is a challenge. Each indoor environment has different sound distributions, different acoustic response, and different acoustic sources. Furthermore, each human occupying the indoor environment has different acoustic traits and occupancy habits. Therefore, developing a detection algorithm that works for all types of indoor environments and humans traits becomes very complex and difficult. 
         [0004]    Rather than focusing on a system that works for most scenarios, a more tractable and cost effective approach would be to design a system that works for certain types of indoor environments and human traits. However, such a system has the disadvantage of being too specific and therefore inflexible. There is room for improvement for human presence detection especially in indoor environments. There is a need for a detection system that is sensitive, accurate, has low computational effort, and sufficiently flexible for different indoor environments and human traits. 
         [0005]    It will be appreciated that these systems and methods are novel, as are applications thereof and many of the components, systems, methods and algorithms employed and included therein. It should be appreciated that embodiments of the presently described inventive body of work can be implemented in numerous ways, including as processes, apparata, systems, devices, methods, computer readable media, computational algorithms, embedded or distributed software and/or as a combination thereof. Several illustrative embodiments are described below. 
       SUMMARY 
       [0006]    A system that detects human presence in an indoor environment using acoustic signals. The system learns the sound characteristics that are specific to the environment and updates the models so as to make the detection more accurate. A detection model is constructed and a decision feedback approach is used to constantly learn and update the statistics of the detection features and sound events that are unique to the environment in question. The learning process may not only rely on acoustic signal, but may also make use of signals derived from other sensors such as range sensor, motion sensors, pressure sensors, and video sensors. 
         [0007]    A system that estimates the pitch and vocal-tract length from speech and classifies the speakers in an indoor environment on the basis of the two estimates. The system learns the pitch and vocal-tract length of the various speakers and performs unsupervised classifications for a prescribed number of speakers. In cases where there are multiple speakers present at the same time, the estimates of the various speakers are constrained to be mutually exclusive thereby resulting in a form of semi-supervised classification. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The inventive body of work will be readily understood by referring to the following detailed description in conjunction with the accompanying drawings, in which: 
           [0009]      FIG. 1  illustrates a block diagram of a system for detecting human presence in an indoor environment; 
           [0010]      FIG. 2  illustrates a more detailed diagram of the feature extraction module; 
           [0011]      FIG. 3  illustrates a more detailed diagram of the human presence and human absence detection models; 
           [0012]      FIG. 4  illustrates a flowchart to refine the joint probability of (a) sound events and human presence (b) sound events and human absence; 
           [0013]      FIG. 5  illustrates a flowchart to refine the prior probability of the input features for the human presence and human absence models. 
           [0014]      FIG. 6  illustrates a block diagram for detecting an individual in an indoor environment 
           [0015]      FIG. 7  illustrates a block diagram for estimating the vocal tract length from speech 
           [0016]      FIG. 8  illustrates a block diagram for estimating the pitch from speech. 
           [0017]      FIG. 9  illustrates a flowchart for preparing the data for semi-supervised classification. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]    A detailed description of the inventive body of work is provided below. While several embodiments are described, it should be understood that the inventive body of work is not limited to any one embodiment, but instead encompasses numerous alternatives, modifications, and equivalents. In addition, while numerous specific details are set forth in the following description in order to provide a thorough understanding of the inventive body of work, some embodiments can be practiced without some or all of these details. Moreover, for the purpose of clarity, certain technical material that is known in the related art has not been described in detail in order to avoid unnecessarily obscuring the inventive body of work. 
         [0019]      FIG. 1  illustrates a block diagram of a system  100  for estimating and improving the speech intelligibility in an enclosure. The system  100  includes a microphone array  102 , a feature extraction module  104 , a human presence processing module  106 , and a model refinement module  108 . 
         [0020]    The feature extraction module  104  extracts the relevant detection features from a microphone array  102 , including other features such as the time of day, day of week, and month of year information. 
         [0021]    The human presence processing module  106  uses the extracted features from module  104  to detect certain acoustic events that may indicate the presence or absence of humans within the environment. The probabilistic estimates of the acoustic events are then used to compute the probability of human presence within the indoor environment. 
         [0022]    The model refinement module  108  refines and updates the detection model. It takes as input the detection features and the probability estimate of human presence, and uses them to refine the prior probability distribution of the features and to establish more accurate correlation between an acoustic event and human presence/absence. 
         [0023]      FIG. 2  illustrates a more detailed block diagram of the feature extraction module  104 . The feature extraction module  104  includes modules to extract the sound direction  104 A, the sound magnitude  104 B, the frequency spectrum  104 C, the autocorrelation coefficients  104 D, and the time information  104 E. 
         [0024]    The sound direction estimator  104 A estimates the direction of arrival (DOA) of sound. One simple approach that may be used by the sound estimator  104 A is to compute the time-delay between the microphones using cross-correlation. 
         [0025]    The sound magnitude estimator  104 B computes the magnitude of each block of the acoustic signal. The magnitude can be computed in several ways such as taking the average power of the block, the average value of the spectral envelope of the block, or the maximum value of the spectral envelop of the block. When computing the average or maximum estimates the frequency spectrum can also give more weightage to certain frequency regions that have greater correspondence to human speech. 
         [0026]    The frequency spectrum estimator  104 C computes the frequency spectrum of the data block and may use several representations such as the fast Fourier transform, wavelet transform, cepstrum, or mel-frequency cepstrum. The autocorrelation coefficients estimator  104 D computes the coefficients directly from the time-domain signal or may do so indirectly from the power spectral density of the signal. The time information computer  104 E can use an in-built timer to obtain the time-of-day, day-of-week, and month-of-year information. 
         [0027]    The feature extraction module  104  may also include a geographical information locator. The geographical location of the indoor environment can provide useful information about the weather, vehicular traffic, population density, which can indirectly affect the external acoustic environment. for e.g., if we know that a certain location has warm weather, the likelihood of an open window will be higher, and this, in turn, increases the likelihood that the external acoustic environment will have greater impact on the indoor acoustic environment. 
         [0028]      FIG. 3  illustrates a more detailed block diagram of module  106  for estimating the likelihood scores for human presence and human absence in an indoor environment. The module  106  has two stages for computing the likelihood for human presence  106 A and  106 C, and two stages for computing the likelihood for human absence  106 B and  106 D. In the first stage of human presence/absence detection modules  106 A and  106 B, the detection probabilities of various acoustic events are computed based on the features obtained from the microphone array and their updated prior probability distributions. Then, in the second stage,  106 C and  106 D, the probability estimates of the various events are combined together to compute the probabilistic scores for human presence and human absence in the considered indoor environment. 
         [0029]    The first stage of the model for estimating the likelihood score for human presence  106 A, include the indoor speech detection model  106 AA, the footstep detection model  106 AB, the household sounds detection model  106 AC, and the electronic audio/music detection model  106 AD. Similarly, the first stage of the model for estimating the likelihood score for human absence  106 B, include the indoor speech detection model  106 BA, the footstep detection model  106 BB, the household sounds detection model  106 BC, and the electronic audio/music detection model  106 BD. 
         [0030]    The indoor speech detection models  106 AA and  106 BA compute the probability that the signal satisfies the following: (1) is human speech, (2) is generated within the indoor environment, and (3) is generated from a human speaker (not electronic device).The model can be realized using either generative models such as hidden Markov models (HMMs), discriminative models such as the support vector machines/logistic regression, or hybrids such as deep neural networks. The features that are important for point (2) above include the magnitude of the speech signal, the DOA of the speech signal, and their prior probability distribution; for example, if it is known beforehand that the speech signal from a certain direction has a higher probability of being generated from an electronic device, the model takes the distribution into account. For point (3), the important parameters include variation in DOA, absence of non-speech acoustics (like music, impulse noise etc.), and prior probability distribution of the DOA, magnitude, and occurrence time with respect to the speech detection event. 
         [0031]    The footstep detection models  106 AB and  106 BB compute the probability that the received acoustic corresponds to human footsteps. The model can be constructed using temporal models such HMMs and trained to detect a large variation of human footsteps, both in high- and low-noise environments. 
         [0032]    The household sounds detection models  106 AC and  106 BC detects other household human-made sounds such as keyboard typing, toilet flush, exhaust fan, and sounds in the kitchen area (e.g., chopping of food, heating in a microwave, opening of water tap, and clanging of utensils/cups/plates/spoons). Learned knowledge of the prior probability distribution of the DOA, signal magnitude, and time of occurrence of the corresponding sounds can be used to enhance the accuracy of the model. 
         [0033]    The electronic audio/music detection models  106 AD and  106 BD computes the probability that the received acoustic signal corresponds to electronically generated speech or music. In general, audio and music from electronic devices have DOAs that are stationary; furthermore, in cases where there are multiple loudspeakers, similar copies of the signal can be received from different directions. If the electronic device is a TV or a music system, then the presence of non-speech signals such as music can be used as criteria for detection. Additionally, learned knowledge of the prior probability distribution of the DOA of electronic audio and time of occurrence can be used to enhance the accuracy of the model. 
         [0034]    The prior probability distributions of input features for the acoustic-event models  106 A and  106 B are gradually updated and become more customized to the indoor environment over time. Note that the prior probability distributions of the input features corresponding to human presence and absence are usually different from one another. 
         [0035]    The second stage of module  106  for estimating the likelihood scores  106 C and  106 D uses the probability estimates of the sound-event detection models in the first stage to arrive at the probability estimate for human presence. To compute the probability, a joint probability distribution of the occurrence of sounds events and human presence, which is learned over time, is used. If A, B, C, and D correspond to each of the sound events in stage one and H p  corresponds to human presence and absence, respectively, then the joint probability is given by P(H p , A, B, C, D). The probabilities of H p  can be computed from the joint probability by marginalizing out the sound events; i.e., 
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         [0036]      FIG. 4  illustrates a flowchart for refining the human presence model  106 C and human absence model  106 D. As can be seen, the initializing model is a generic model that is trained from a database of different indoor environments and sound-event scenarios. Over time, the event statistics of the two models are gradually updated so that they become more specific to the considered indoor environment. From the flowchart, we observe that there are two ways to select the events in order to update the event statistics: In the first case, the model is explicitly notified of human presence (or absence), while in the second case only those events that result in a high probabilistic score of human presence (or absence) are selected for updating. 
         [0037]      FIG. 5  illustrates the flowchart where the prior probability distribution of the acoustic event models corresponding to human presence and human absence are updated to gradually reflect the characteristics of the considered indoor environment. As the priors become more specific to the indoor environment, the acoustic event models become more accurate. 
         [0038]    Although the foregoing has been described in some detail for purposes of clarity, it will be apparent that certain changes and modifications may be made without departing from the principles thereof. It should be noted that there are many alternative ways of implementing both the processes and apparatuses described herein. Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the inventive body of work is not to be limited to the details given herein, which may be modified within the scope and equivalents of the appended claims. 
         [0039]      FIG. 6  illustrates a block diagram of a system  300  for identifying an individual in an indoor environment. The system  300  includes a microphone array  302 , a vocal tract length estimation module  304 , a pitch estimation module  306 , and a model  308  to identify an individual. 
         [0040]    On the basis of the pitch estimate and the vocal-tract-length estimate, the likelihood of being generated from each of the individuals&#39; model is computed. The individual whose corresponding model gives the maximum likelihood estimates is then selected. 
         [0041]      FIG. 7  illustrates a more detailed block diagram of a system  304  for extracting the formant peaks features, since they directly correlate with the vocal tract length. The system  304  includes a schwa-like vowel detector  304 A, a formant-peak estimation module  304 C, a DOA estimation module  304 D, a multiple-speaker indicator module  304 E, and a storage module  304 F for classification. 
         [0042]    The estimated formant peaks are stored in the storage module  304 F. If there are multiple speakers present during the same time, the estimated formant peaks from the various speakers are appropriated labeled so that they are always mutually exclusive from each other during classification. 
         [0043]      FIG. 8  illustrates a more detailed block diagram of a system  306  for extracting the pitch of the speech signal. The system  306  includes a speech vowel detector  306 A, a pitch estimation module  304 C, a DOA estimation module  304 D, a multiple-speaker indicator module  304 E, and a storage module  304 F for classification. 
         [0044]    The estimated pitch is stored in the storage module  306 F. If there are multiple speakers present at the same time, the estimated pitch values from the various speakers are appropriated labeled so that they are always mutually exclusive from each other during classification. 
         [0045]      FIG. 9  illustrates a flowchart for labeling the estimates from multiple speakers that are simultaneously present so that they are mutually exclusive during classification. For classification, clustering algorithms such as K-means algorithm, mixture models, and hidden Markov models can be used.