Patent Application: US-201013512139-A

Abstract:
a method for detecting , in an audio signal including a stream of audio samples , ), ticks in a noisy environment , comprises the steps of applying the signal to a coarse tick detection processor arranged to decide whether it is likely that the signal includes a tick and , only then , enabling a fine tick detection processor to decide , by more thorough processing the audio signal , whether the audio signal represents an audio tick . the coarse processing step preferably includes buffering audio samples , ), determining the maximum and minimum values of each sample , forming their local range , delaying the local range with one or more samples and comparing the difference between the actual local range and the delayed local range with a threshold value , and , if the difference exceeds the threshold value , outputting a trigger for enabling the fine detection processor the fine processing step preferably includes buffering audio samples , ), computing , for each buffer content ), a fast fourier transformed buffer content ), determining the difference ) between the resulting frequency components , comparing the difference ) to the content of a previously trained fingerprint set ) and calculating a correlation coefficient ) between them , comparing the correlation coefficient to a threshold value and , if the correlation coefficient exceeds the threshold value , outputting a signal indication the presence of a tick in the audio signal .

Description:
fig1 shows that an audio signal y ( n ) which is investigated whether it includes a portion representing a “ tick ”, is fed to an ( optional ) preprocessing module ( shown in fig2 more in detail ) in which the signal is normalized in order to facilitate further processing . the preprocessed signal y ′( n ) is fed to a coarse tick detection processor ( shown in fig3 more in detail ) which decides whether it is likely that the signal ( part ) y ′( n ) represents a tick or not . in the first case a trigger value n c is set which enables — represented by a switch — to feed the preprocessed signal y ′( n ) to a fine detection processor ( shown in figure fig4 more in detail ) in order to have it processed there . absence of the trigger disables the fine processing . due to this enabling / disabling control output by the coarse processor the fine detection processor ( which requests relatively much processing capacity ) is solely enabled when the coarse processor ( which does not need much processing capacity ) “ suspects ” the presence of a “ tick ” in an audio signal portion and converts this “ suspection ” into enabling trigger . it is noted that , although the enabling / disabling function has been represented by a switch ( symbol ), this enabling / disabling function can be implemented in several other ways . important is that the fine processor is solely activated or involved ( anywise ), by means an “ enable ” trigger ( signal ) from the coarse processor . fig2 shows the preprocessing module in which the difference y ′( n ) is generated between each audio sample y ( n ) and its previous audio sample y ( n − 1 ). this action is performed by the shown digital delay circuit . fig3 illustrates , at the left side of the figure , the construction of an input buffer for the coarse processor which is always loaded with 75 % overlapping audio samples . in that way the buffer collects the last 16 normalized samples whereafter the maximum and minimum values of the collected samples can be selected . the difference between these values determine the local range . in a next step the local range is delayed with two samples . the difference between the current range and the delayed range is tested by a threshold : if the difference is larger than a threshold value t c then a trigger n c is produced , causing enablement of the fine detection processor ; otherwise no such trigger is given . fig4 presents an embodiment of a fine detection processing module , where a fast fourier transform is computed for each buffer , and the difference is determined between the resulting frequency components . this difference is compared then to a ( previously stored ) trained reference ( top right ) using a correlation coefficient . this configuration is scale invariant , causing that both soft and heavy touches ( ticks ) are detected correct . the trained reference may be obtained for example by signaling when a touch is generated , determining properties of the audio signal at that time and storing the generated properties , or storing averages of the properties obtained with respective touches . training may also comprise determining relative weights that may be assigned to different properties , based on the observed spread in the values obtained using respective touches during training . training may have the advantage that detection accounts for the particular mechanical vibration modes of the device that is touched and used to receive the audio signal ( e . g . the vibration modes of its housing ). summarizing the complete processing when the fine processor , under control of the coarse processor output n c , has been enabled , the content y ( n ), recorded by e . g . the microphone of a mobile device is normalized to a zero - mean signal y ′( n ) which is buffered with ( e . g . 75 %) overlap in the time domain , resulting in a matrix b p ( n , m ). fourier transformation ( fft ) is performed on that buffer content b p ( n , m ) resulting in a matrix e c ( w , m ) which is also buffered with overlap in the time domain . finally the frequency wise difference between the ( fft ) buffers is calculated , resulting in a matrix f c ( w , m ), also buffered with overlap in the time domain , which matrix f c ( w , m ) is compared to a reference matrix f *( ω , m ). when the resulting correlation coefficient exceeds a predetermined threshold value , the microphone signal is deemed to include a “ tick ”, which can be used as ( part of ) a pairing protocol as e . g . disclosed in wo 2009 / 014438 . a more detailed discussion of the coarse and fine audio signal processing steps will be given below . a sample of the audio signal recorded by a mobile device is given by y ( n ) ( y ( n ) ε [− 1 , 1 ]), where n ( nεn ) is its sample number . the audio signal is sampled with a frequency f with unit samples per second . the touches in this example touches were sampled with a sample frequency of 11025 hz . the audio signal could be biased with a constant , which suppressed by taking the sample difference : actually , this is a basic high pass filter . in the coarse approach a touch is detected , if the local difference between maximum and minimum signal range increases significantly . this point of change represents the start of the touch . this sample is detected as follows . the signal is buffered using a first - in - first - out ( fifo ) buffer that contains n c ( n c εn ) samples . this buffer is given by b c ( n ) = b c [ y ′] ( n ) ={ y ′ ( n − n c + 1 ) , . . . , y ′( n − 1 ) , y ′( n )}. ( 2 ) from this buffer the difference between maximum and minimum is computed , which represents the signal range r in the last n c samples : if this range increases with more than t c , then a coarse touch δ ′ is detected . as soon as a coarse touch is detected , another touch cannot be detected within the next n c samples . fig1 presents the detection of the coarse touch sample for an example . the sample n c is the start sample of a touch : this approach detects most touches , also within noisy environments . therefore , the false negative rate is rather high . on the other hand it detects more than touches , each event that causes a change in the range of the audio signal is identified as a touch . therefore , the next stage explores another signal property of a touch to reduce the false negative rate . other types of course detection may be used , such as detection of output pulses based on a comparison of pulse amplitude with signal amplitude in a time interval that surrounds the pulse . precise touch detection exploits the signal characteristic of a touch . a primitive model of a touch is a pulse , and an important property of a pulse is that its fourier transform ( ft ) is a uniform energy spectrum for all frequency components according to [ 2 ]. this property is exploited using the ft applied to fifo buffers before and after a coarse touch . in total n b ( n b εn ) buffers are filled containing n p ( n p εn ) samples with p ( pε [ 0 , 1 ]) percentage of overlap . the last sample in buffer m ( mε { 0 , . . . , n b − 1 }) for coarse touch n c is given by n c ′ ( m ) = n c + mn p ( 1 − p ), ( 6 ) b p ( n c , m ) ={ y ′ ( n c ′ ( m ) − n p ) , . . . , y ′( n c ′ ( m )− 2 ) , y ′( n c ′ ( m )− 1 )}. ( 7 ) note that the first buffer ( m = 0 ) does not contain sample n = n c , which was the first sample of a touch . for each buffer is the ft computed , such that the energy of each frequency component ( ω ) is given by e c ( ω , m )= log 10 |[ b p ( n c , m )]( ω )|. ( 8 ) the fast ft is used to compute the energy spectrum fast . for this reason the number samples in a buffer is a power of two : n p ε2 k , kε1 , 2 , . . . . the energy of the frequency components is used to represent a reference , which will be called a fingerprint of a touch , which is given by fig2 presents this fingerprint as an image . the difference is only computed if the energy components exist , such that m ′ ε { 0 , . . . , n b − 3 }. the difference between the energy components is taken for three reasons : 1 . the difference between buffers with and without touch properties capture the touch properties . 2 . the recorded signal y is a superposition of a touch plus background signals , which we assume stationary within the buffers . influence of these background signals is suppressed by this difference . 3 . due to this difference the average is by definition equal to zero , such that it is allowed to compute a correlation coefficient to compare fingerprints according to [ 1 ]. from multiple touches the optimal fingerprint f * is learned as its average . this fingerprint is device specific on which the touch is detected . this fingerprint is compared to the measured fingerprint ( f c ) using the correlation coefficient ( ρε [− 1 , 1 ]): most important for robust fingerprint computation is that the start sample of a touch ( n c ) is detected correctly , such that the fingerprint exploits always the same touch characteristics . this solution is innovative . touch detection is based on quite some parameters . table 1 presents an overview of these parameters with their range of use . although an advantageous example of a comparison of properties of the audio signal with the fingerprint has been illustrated by means of correlation of spectral properties , it should be appreciated that other ways of comparing may be used . for example , the values of the spectral properties may be compared directly , the tick being detected when no difference exceeds a threshold , or no more than a predetermined number of differences exceeds a threshold , or an aggregate difference does not exceed a threshold . instead of spectral values other properties may be compared , for example the results of filtering the audio signal with different predetermined filters , or a time correlation may be used . in summary , coarse touch detection presents an efficient method to detect touches , because the used operators are simple : buffer , maximum , minimum , difference and a comparison . precise touch detection is not efficient , because a fourier transform is computed . since it is only computed , if a coarse touch is detected , it achieves a minimum processor and power use . although coarse and fine touch detection from an audio signal have been described , the skilled person will appreciate that the detection method does not depend on whether the sound arises from a touch . nor does it depend on the type of objects that are touched . however , almost all tick sounds are produced by touching objects . according to one aspect the method is applied to mobile communication devices ( commonly referred to as mobile devices in the telecommunication art ), in order to form pairs of such devices as explained in wo 2009 / 014438 . as noted in this document the detected touch may be a touch of the mobile devices themselves , or external touches such as clapping hands within detection range from the mobile devices . each mobile device has a microphone and a processing unit . an audio identifier may be produced by tapping mobile devices together . the tapping sound is received by the microphones of both mobile devices and the audio signals registered by the microphones of the respective devices is supplied to the processing units of the respective devices . the processing unit of the devices may comprise the preprocessing module , the coarse processing module and / or the fine processing module . these modules may be realized for example using a programmable processor and program modules with programs of instructions for the programmable processor that make the programmable processor perform the functions of the preprocessing module , the coarse processing module and / or the fine processing module . the embodiments may be used in methods and systems for matching proximate devices , and also in devices , such as servers , for use in such systems . an example of a system for matching proximate devices is disclosed in wo 2009 / 014438 mentioned above . a method of identifying proximate mobile devices , may be used wherein the method comprises a first mobile device detecting a tick , a second mobile device detecting the tick , the first mobile device sending a request message associated with the tick to the second mobile device , and the second mobile device , upon receipt of the request message , sending an acknowledgement message to the first mobile device so as to establish mutual identification . the request message and / or the acknowledgement message may comprise information about the detected tick , so that at least one device is enabled to verify that the devices have detected the same tick by comparing the information about the detected tick from both devices . alternatively , a server may perform the verification and send confirmation messages to one or both mobile devices , or establishes a communication channel between the mobile devices if the information about the detected tick from both devices matches . alternatively the devices may send request messages with their information about the tick to the server and the server may send acknowledgement messages to a pair of devices when it is verified that these devices have both detected the same tick . advantageously , the embodiments may be utilized in consumer devices , such as mobile telephone devices , pdas ( personal digital assistants ), computers , in particular portable ( e . g . laptop and notebook ) computers , electronic book viewers , and other consumer devices . in an embodiment the coarse tick detection processor is part of a mobile device and the fine tick detection processor is part of a server . in this case the mobile device may be configured to perform coarse tick detection and to transmit selected audio signal data to the server in response to detection of a tick by said coarse tick detection , the selected audio signal data comprising audio signal data that resulted in the coarse tick detection . audio signal data from a time window of predetermined length may be selected that comprises a time point at which the coarse tick was detected . in this embodiment the server receives the selected audio signal data and applies the fine tick detection to the audio signal data . a system is provided for detecting ticks in a noisy environment , from an audio signal which includes a stream of audio samples ( y ( n ), ( y ′( n )), the system comprising a coarse tick detector and a fine tick detector , wherein the course tick detector is arranged to perform an initial detection of ticks from the audio signal and to trigger the fine tick detector to perform fine tick detection only when the course tick detector has made an initial detection of a tick , the fine tick detection comprising more thorough processing of said audio signal than course tick detection . in an embodiment the system comprises a mobile communication device with a microphone , coupled to the coarse tick detector and a fine tick detector to supply the audio signal , the fine tick detector being configured to compare properties of the audios signal with reference properties that are specific to the mobile device , the coarse tick detector only performing generic processing without a comparison with reference properties that are specific to the mobile device . the system may be configured to provide information to the mobile communication device that establishes the identity of another mobile communication device that was used to generate the tick by touching the mobile communication device , or to open a communication channel between the devices , triggered by fine detection . it is noted that any terms used in this document should not be construed so as to limit the scope of the invention . in particular , the words “ comprise ( s )” and “ comprising ” are not meant to exclude any elements not specifically stated . single ( circuit ) elements may be substituted with multiple ( circuit ) elements or with their equivalents . it will be understood by those skilled in the art that the invention is not limited to the embodiments illustrated above and that many modifications and additions may be made without departing from the scope of the invention as defined in the appending claims . a . leon - garcia : probability and random processes for electrical engineering . number 0 - 201 - 50037 - x . addison - wesley publishing company , inc ., second edition , 1994 . b . porat : a course in digital signal processing . 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