Patent Publication Number: US-9426373-B2

Title: Whiteboard activity noise detection system for immersive telepresence systems

Description:
TECHNICAL FIELD 
     The disclosure relates generally to conferencing systems. More particularly, the disclosure relates to allowing a determination to be made as to when there is activity on a whiteboard during a telepresence session such that a camera may efficiently focus on the whiteboard when there is activity on the whiteboard. 
     BACKGROUND 
     The use of collaborative systems, e.g., conferencing systems such as immersive telepresence systems, enables parties who are not at the same physical location to efficiently assemble such that information may be shared. Many conference sessions include the use of a whiteboard which allows one participant in a conference session to write on the whiteboard while other participants in the conference session may see what is written on the whiteboard. When a conference session includes at least one participant who is participating from a remote location, e.g., is not in the same room as a whiteboard that is in use, a camera may focus and zoom in on the whiteboard so that the participant who is participating from the remote location may view the whiteboard. 
     A whiteboard may generally be an important feature of an immersive telepresence system. However, as a whiteboard may not be in use all the time during a session, a camera within an immersive telepresence session may be controlled to zoom in on the whiteboard substantially only when the whiteboard is in use. While a camera may be manually controlled by a user interface device to zoom in on the whiteboard when the whiteboard is in use, it is often inefficient for an individual to be responsible for manually controlling the camera. 
     Many immersive telepresence systems include a microphone that is specifically positioned to pick up sound in the vicinity of the whiteboard. In some instances, when a microphone positioned near a whiteboard detects voice activity, the camera may be automatically controlled to zoom in on the whiteboard. However, as an individual talking near the whiteboard may not necessarily be writing or drawing on the whiteboard, zooming in on the whiteboard when voice activity is detected at the whiteboard may not be effective in efficiently zooming in on the whiteboard when the whiteboard is in use. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosure will be readily understood by the following detailed description in conjunction with the accompanying drawings in which: 
         FIG. 1A  is a diagrammatic representation of an environment suitable for a telepresence session which includes a whiteboard and a plurality of microphones in accordance with an embodiment. 
         FIG. 1B  is a diagrammatic representation of a process of a camera focusing on a whiteboard during a telepresence session based upon detected activity on a whiteboard in accordance with an embodiment. 
         FIG. 2  is a process flow diagram which illustrates a method of using a camera during a telepresence session such that a determination of whether the camera is to focus on a whiteboard is based on whether there is activity on the whiteboard in accordance with an embodiment. 
         FIG. 3  is a process flow diagram which illustrates a method of analyzing sound detected by a microphone that is near a whiteboard, e.g., step  217  of  FIG. 2 , in accordance with an embodiment. 
         FIG. 4  is a process flow diagram which illustrates a method of determining whether a signal from a whiteboard microphone indicates activity on a whiteboard, e.g., step  309  of  FIG. 3 , in accordance with an embodiment. 
         FIG. 5  is a diagrammatic representation of a system which processes microphone signals to determine if whiteboard activity is detected in accordance with an embodiment. 
         FIG. 6  is a diagrammatic representation of a system which processes a signal from a whiteboard microphone to determine if erasing noise is detected in accordance with an embodiment. 
         FIG. 7  is a diagrammatic representation of an erasing noise detection system, e.g., erasing noise detection module  672  of  FIG. 6 , in accordance with an embodiment. 
         FIG. 8  is a diagrammatic representation of a control system arranged to demodulate a signal in accordance with an embodiment. 
         FIG. 9  is a diagrammatic representation of a system which processes a signal from a whiteboard microphone to determine if writing noise is detected in accordance with an embodiment. 
         FIG. 10  is a block diagram representation of a server configured to determine when there is activity on a whiteboard and to control a camera to focus on the whiteboard when there is activity on the whiteboard in accordance with an embodiment. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     General Overview 
     According to one aspect, a method includes obtaining a first signal from a first microphone, and determining when the first signal is indicative of activity on a first surface. The method also includes controlling a camera to focus on the first surface when it is determined that the first signal indicates the activity on the first surface. In one embodiment, the first microphone and the camera are part of a collaboration system, and the first surface is a surface of a whiteboard. 
     Description 
     Many collaboration systems, e.g., immersive telepresence systems, utilize whiteboards to present and to share information. Whiteboards may generally include, but are not limited to including, marker boards, dry-erase boards, and/or any suitable surface on which writing instruments, e.g., markers or pens, may be used to write. A camera of a collaboration system is often used to film a location that is associated with the collaboration session such that remote participants in the collaboration session, e.g., participants who are virtually attending the collaboration session, may effectively see what is happening at the location. A whiteboard at a location that is associated with a collaboration system may be captured or otherwise recorded by a camera when the whiteboard is in use such that remote participants in the collaboration system may see information that is being presented on the whiteboard. 
     Accurately determining when a whiteboard is in use during a collaboration session allows a camera focus on or zoom in on the whiteboard substantially only when the whiteboard is in use, and may reduce the likelihood of the camera focusing on or zooming in on the whiteboard when there is no actual activity with respect to the whiteboard. When a user of a whiteboard, e.g., a presenter, uses a whiteboard during a presentation, the user may draw on a surface of the whiteboard, write on the surface of the whiteboard, and/or erase the surface of the whiteboard. Drawing, writing, and erasing activities may cause specific noises, and those specific noises may be captured or otherwise picked up by a microphone. The noises captured by the microphone are processed using audio signal processing methods, and if the processing identifies drawing, writing, and erasing activities relating to a whiteboard, then a camera may zoom in on the whiteboard. 
     In one embodiment, when noise activity is detected with respect to a surface of a whiteboard during a collaboration session, a camera may zoom in on the whiteboard. Noise activity that is detected with respect to a surface of a whiteboard includes erasing noise, e.g., noise associated with swiping an eraser along the surface of the whiteboard, and writing noise, e.g., noise associated with a writing instrument contacting the surface of the whiteboard when writing and/or drawing occurs. 
     By detecting noise activity on the surface of a whiteboard, a collaboration system such as an immersive telepresence system may more accurately determine when the whiteboard is in use. Noise activity on the surface of a whiteboard generally only occurs when someone is writing on the whiteboard or erasing the whiteboard and, as such, when a camera zooms in on the surface of the whiteboard when noise activity is detected, there is a relatively high probability that the whiteboard is in use. It should be appreciated that noise associated with writing on a surface of a whiteboard may generally include noise associated with drawing on the surface of the whiteboard. 
     Referring initially to  FIG. 1A , a location suitable for a collaboration session such as an immersive telepresence session will be described in accordance with an embodiment. An environment or location  100 , e.g., a conference room, generally includes a conference table  104 , a whiteboard  108 , and a camera  116 . Typically, a conference table  104  may be arranged such that multiple participants in a collaboration session may sit around conference table  104 . It should be appreciated, however, that in lieu of sitting around conference table  104 , participants may instead sit in a seating area (not shown) or in rows of chairs (not shown) at location  100 . In other words, location  100  is not limited to including conference table  104 , and may generally include any suitable arrangement, e.g., rows of chairs, which allow participants in the collaboration session to gather. 
     Location  100  generally includes a plurality of microphones  112   a ,  112   b . In the embodiment as shown, microphone  112   a  is arranged to pick up sound in the vicinity of conference table  104 , while microphone  112   b  is arranged to pick up sound near whiteboard  108 . As will be appreciated by those skilled in the art, microphone  112   a  may also pick up sound from whiteboard  108 , and microphone  112   b  may also pick up sound from conference table  104 . That is, while microphone  112   a  is generally positioned to capture sounds from near conference table  104 , microphone  112   a  may also capture other sounds, e.g., sounds from other areas within location  100 . Similarly, while microphone  112   b  is generally positioned to capture sounds from near whiteboard  108 , microphone may also capture other sounds. However, microphone  112   a  may be considered to be an array microphone arranged to capture sound activity from near conference table  104 , and microphone  112   b  may be considered to be a whiteboard microphone arranged to capture sound activity from near whiteboard  108 . 
     Microphones  112   a ,  112   b  and camera  116  may generally be part of an overall collaboration system which supports a collaboration session. By way of example, microphones  112   a ,  112   b  may capture sounds during a collaboration session, and camera  116  may capture images during the collaboration session. The captured sounds and images may be provided to a server (not shown) arranged to process the captured sounds and images, e.g., to determine when there is activity on whiteboard  108 . Camera  116 , which may be controlled based on where activity occurs at location  100 , may be arranged to zoom in on, or otherwise focus on, whiteboard  108  when activity is detected on whiteboard  108 . For example, when microphone  112   b  detects noise activity such as writing noise or erasing noise, camera  116  may move or otherwise be adjusted to zoom in on whiteboard  108 , as activity is effectively detected on whiteboard  108 . 
       FIG. 1B  is a diagrammatic representation of a process of a camera focusing on a whiteboard during a collaboration session associated with a location, e.g., location  100  of  FIG. 1A , based upon detected activity on a whiteboard in accordance with an embodiment. At a time t 1 , a collaboration session such as an immersive telepresence session is in progress, and camera  116  is focused on conference table  104 . During the course of the collaboration session, microphone  112   b  detects activity on whiteboard  108  at a time t 2 . Activity on whiteboard  108 , e.g., activity on a writing surface of whiteboard  108 , may effectively be detected by microphone  112   b , or by a server (not shown) which analyzes sound data provided by microphone  112   b , when noise activity such as erasing noise or writing noise is identified or perceived. Once activity on whiteboard  108  is detected, camera  116  switches from focusing on the conference table to zooming in on or otherwise focusing on whiteboard  108  at a time t 3 . 
     With reference to  FIG. 2 , an overall method of managing a session, as for example an immersive telepresence session, will be described in accordance with an embodiment. A method  201  of managing a session begins at step  205  in which a session is initiated  205 . Initiating a session may include, but is not limited to including, dialing into a conference bridge. In the described embodiment, the session includes at least one party that participates in the session from a location that includes a whiteboard and a plurality of microphones. 
     Once the session is initiated, a determination is made in step  209  as to whether sound is detected by the microphone located nearest to the whiteboard, or a whiteboard microphone. If the determination is that there is no sound, or an insignificant amount of sound, detected by the whiteboard microphone, then the session continues in step  213 . It should be appreciated that the session may continue such that the camera is focused using a default process, e.g., the camera may be focused on parties around a conference table unless it is determined that there is activity on the whiteboard. From step  213 , process flow returns to step  209  in which it is determined if sound is detected by the whiteboard microphone. 
     Alternatively, if it is determined in step  209  that there is sound detected by the whiteboard microphone, the sound is analyzed in step  217 . One method of analyzing sound detected by the whiteboard microphone will be discussed with respect to  FIG. 3 . After the sound detected by the whiteboard microphone is analyzed, it is determined in step  221  whether the sound indicates that there is activity on the whiteboard. That is, it is determined in step  221  whether erasing noise or writing noise is indicated. If it is determined that the sound does not indicate activity on the whiteboard, then the indication is that there is that the whiteboard is not currently in use during the session. As such, process flow moves to step  213  in which the session continues with the camera focused using a default process. 
     If, on the other hand, it is determined in step  221  that sound detected by the whiteboard microphone indicates activity on the whiteboard, then the session continues in step  225  with the camera zoomed in on or focused on the whiteboard. Once the camera is zoomed in on the whiteboard, process flow returns to step  209  in which it is determined if sound continues to be detected by the whiteboard microphone. 
     With reference to  FIG. 3 , a method of analyzing sound detected by a whiteboard microphone, e.g., step  217  of  FIG. 2 , will be described in accordance with an embodiment. A method  217  of analyzing sound detected by a whiteboard microphone begins at step  305  in which signals from an array microphone and from a whiteboard microphone are obtained. In the described embodiment, the array microphone and the whiteboard microphone are both located at a location, e.g., a conference room, with the array microphone located substantially away from a whiteboard and the whiteboard microphone located in relatively close proximity to the whiteboard. 
     A determination is made in step  309  as to whether the signal from the whiteboard microphone indicates activity on the whiteboard, or noise associated with activity on the whiteboard. One method of determining whether the signal from the whiteboard activity indicates activity on the whiteboard will be discussed below with reference to  FIG. 4 . If the signal from the whiteboard microphone does not indicate activity on the whiteboard, then the indication is that the sound picked up by the whiteboard microphone is voice activity or other activity that is not activity on the whiteboard. As such, the process of analyzing sound from a whiteboard microphone is completed. 
     Alternatively, if it is determined in step  309  that the signal from the whiteboard microphone indicates activity on the whiteboard, process flow proceeds to step  313  in which it is determined if the signal from the whiteboard microphone indicates erasing noise. That is, it is determined in step  313  whether erasing of the whiteboard is indicated. If the determination in step  313  is that erasing noise is not indicated, the indication is that writing noise is indicated. As such, in step  321 , the noise associated with activity on the whiteboard, or whiteboard noise, is identified as writing noise. In one embodiment, a detection threshold that corresponds to the detection of writing noise may be adjusted as appropriate, although it should be appreciated that a detection threshold may have a substantially default value that is not typically adjusted in step  321 . After the whiteboard noise is identified as writing noise, the method of analyzing sound from a whiteboard microphone is completed. 
     Returning to step  313 , if it is determined that the signal from the whiteboard microphone indicates erasing noise, then the whiteboard noise is identified as erasing noise in step  317 . In one embodiment, a detection threshold that corresponds to the detection of erasing noise may be adjusted as appropriate, although it should be appreciated that a detection threshold may have a substantially default value that is not typically adjusted in step  317 . Upon identifying the whiteboard noise as erasing noise, the method of analyzing sound from a whiteboard microphone is completed. 
       FIG. 4  is a process flow diagram which illustrates a method of determining whether a signal from a whiteboard microphone indicates activity on a whiteboard, e.g., step  309  of  FIG. 3 , in accordance with an embodiment. A method  309  of determining whether a signal from a whiteboard microphone indicates activity on a whiteboard begins at step  405  in which a noise floor is determined. 
     As will be appreciated by those skilled in the art, a noise floor may be a substantially average noise level at a location, as for example in a room, when no one is speaking. In general, a noise floor may be substantially constant over time. Any suitable noise floor estimation method may be used to estimate a noise floor. For example, a slow up/fast down estimator may be used to estimate a noise floor. When a current sound signal level is higher than a previous estimated level, an estimate may effectively go up at a slow and fixed step. When a current sound signal level is lower than a previously estimated level, an estimate may go down faster and in proportion to an error between a new low level and a previous estimate. 
     In step  409 , the noise floor is compared to the signal power from an array microphone and the signal power from a whiteboard microphone to determine whether voice activity is detected at a whiteboard. A determination is then made at step  413  as to whether voice activity is detected at the whiteboard. If it is determined that voice activity is detected at the whiteboard, a whiteboard noise detection threshold is reduced in step  417 . Reducing the whiteboard noise detection threshold when voice activity is detected at the whiteboard effectively accounts for an increased likelihood that a whiteboard is being written on or being erased when there is someone speaking near the whiteboard. In other words, if a voice is detected in proximity to a whiteboard, a whiteboard noise detection threshold may be reduced as it may generally be more likely that a whiteboard is in use when a person is speaking hear the whiteboard. 
     After the whiteboard noise detection threshold is reduced in step  417 , process flow moves to step  421  in which it is determined whether the signal from the whiteboard microphone indicates erasing noise. The signal from the whiteboard microphone may indicate erasing noise when narrow band noise, as for example noise between approximately three kiloHertz (KHz) and approximately five KHz, is detected. The narrow band noise may be generated by friction of an eraser as it is swiped on the surface of a whiteboard. 
     When the signal from the whiteboard microphone indicates erasing, activity on the whiteboard is identified as erasing activity in step  425 . Once the activity on the whiteboard is identified as erasing activity, the method of determining whether a signal from a whiteboard microphone indicates activity on a whiteboard is completed. Alternatively, if the determination in step  421  is that the signal from the whiteboard does not indicate erasing, then the implication is that the activity on the whiteboard is writing activity. The signal from the whiteboard microphone may indicate writing noise when there is a click noise pulse associated with a pen or a marker tapping on the whiteboard and/or narrow band noise associated with the pen or the marker sliding across the whiteboard. A click noise pulse may have a spectrum of sliding noise in the range of approximately 500 Hertz (Hz) and approximately 800 Hz, while narrow band noise associated with a pen or a marker sliding across a whiteboard may have a spectrum of sliding noise in a range between approximately 3000 Hz and approximately 5000 Hz. When the signal from the whiteboard microphone indicates writing, the activity on the whiteboard is identified as writing activity in step  429 , and the method of determining whether a signal from a whiteboard microphone indicates activity on a whiteboard is completed. 
     Returning to step  413 , if it is determined that voice activity is not detected at the whiteboard, then the indication is that either erasing activity or writing activity has been detected. As such, process flow moves to step  421  in which it is determined whether the signal from the whiteboard microphone indicates erasing noise. 
     Systems, as for example control systems, used to identify noise activity associated with erasing a surface of a whiteboard and/or noise activity associated with writing on the surface of the whiteboard, and used to control a camera to zoom in on the surface of the whiteboard as appropriate may vary widely. With reference to  FIGS. 5-9 , examples of such systems will be described in accordance with an embodiment. 
     When a party, as for example a presenter, is using a whiteboard and speaking at the same time, some amount of the whiteboard noise activity may be obscured or substantially buried in a voice signal. In one embodiment, if whiteboard voice activity is detected, a noise detection threshold may be lowered to increase the likelihood that obscured or substantially buried whiteboard noise activity may be detected. As will be appreciated by those skilled in the art, a location such as a meeting room, both a whiteboard microphone and an array microphone may detect voice activity. Voice activity may generally be detected using any suitable method. When both a whiteboard microphone and an array microphone detect voice activity, the whiteboard microphone signal may be compared with the array microphone signal to determine whether the voice activity is occurring substantially in front of a whiteboard, e.g., whether the voice activity is speech uttered by a presenter standing at the whiteboard. To determine whether voice activity is occurring substantially in front of a whiteboard, relative signal strengths and signal arrival times of a whiteboard microphone signal and an array microphone signal may be compared. 
       FIG. 5  is a diagrammatic representation of a system which processes microphone signals to determine if whiteboard activity is detected in accordance with an embodiment. A whiteboard microphone signal is processed by a signal power module  524   a  of the whiteboard microphone signal to determine a signal power of the whiteboard microphone signal, or to obtain an estimate of the signal power of the whiteboard microphone signal. Information from signal power module  524   a  of the whiteboard microphone signal is used by a noise floor estimation module  528   a , a voice activity detection module  532   a , and a whiteboard voice activity detection module  536 . Noise floor estimation module  528   a  produces a noise floor estimation based on the estimated signal power, as for example using a low pass filter such as a fast-down/slow-up low pass filter. The noise floor estimation is also used by voice activity detection module  532   a . For example, an output of noise floor estimation module  528   a  and an output of signal power module  524   a  may be used to determine whether any voice activity is detected by voice activity detection module  532   a . That is, a noise floor estimation and a signal power of a whiteboard microphone signal may be used to determine whether any whiteboard voice activity is detected. 
     An array microphone signal, or a signal from a microphone that is relatively away from a whiteboard, is processed by a signal power module  524   b  to determine, e.g., to estimate, a signal power of the array microphone signal. Information relating to the signal power of the array microphone signal is used by a noise floor estimation module  528   b , a voice activity detection module  532   b , and whiteboard voice activity detection module  536 . A noise floor estimated by noise floor estimation module  528   b  may be used by voice activity detection module  532   b  to ascertain whether voice activity is detected. 
     When voice activity is detected by voice activity detection module  532   a  and voice activity detection module  532   b , the relative strengths of the whiteboard microphone signal and the array microphone signal are compared by whiteboard voice activity detection module  536  to detect whiteboard voice activity. That is, once signals are provided to whiteboard voice activity detection module  536 , whiteboard voice activity detection module  536  determines whether there is voice activity detected in a collaboration environment, or a location associated with a collaboration session. Using information relating to whether voice activity is detected in proximity to a whiteboard, a whiteboard noise activity detection module  540  uses that information and the whiteboard microphone signal to determine whether whiteboard noise activity is present. As previously mentioned, at least one threshold used to determine whether a whiteboard microphone signal indicates the existence of whiteboard noise activity may be lowered if it is determined that there is voice activity in proximity to a whiteboard. 
     Whiteboard erasing noise detection is generally based on the detection of narrow band noise that is generated by contact, e.g., friction, of an eraser with a whiteboard. When an eraser is used to erase a surface of a whiteboard, the erase is often moved in a back- and forth motion. Narrow band noise may be associated with erasing a surface of a whiteboard may have frequencies between approximately three KHz and approximately five KHz.  FIG. 6  is a diagrammatic representation of a system which processes a signal from a whiteboard microphone to determine if erasing noise is detected in accordance with an embodiment. A whiteboard microphone signal is provided to a low pass filter  644 , and is then down sampled by a down sampling module  548 . Down sampling may reduce the amount of computation needed to identify erasing noise within a whiteboard microphone signal. For instance, the signal provided by low pass filter  644  may be sampled at a sampling rate of approximately 48 kHz, and may be down sampled to approximately 12 kHz with a 4:1 down sampling rate. 
     A down sampled signal is provided to a high pass filter  670 , a low pass filter  668 , and a demodulator module  652 . High pass filter  670 , which is configured to isolate high frequency portions or signals of the down sampled signal, may be set with any suitable cut-off frequency. By way of example, high pass filter  670  may have a cut-off frequency of approximately 4.5 KHz such that frequencies in the down sampled signal that are greater than approximately 4.5 KHz may pass through high pass filter  670 . Low pass filter  668  is configured to isolate low frequency portions or signals of the down sampled signal, and may have any suitable cut-off frequency, e.g., a cut-off frequency of approximately 3.5 KHz such that frequencies in the down sampled signal that are less than approximately 3.5 KHz may pass through low pass filter  668 . Demodulator module  652  is arranged to demodulate the down sampled signal in order to identify a narrow band signal. Demodulator module  652  may demodulate the down sampled signal using any suitable frequency fc 1 . In one embodiment, demodulator module  652  may demodulate the down sampled signal using a demodulation frequency of approximately four KHz. 
     Relatively high frequency signals that pass through high pass filter  670  are provided to a signal power module  660   c  that estimates the signal power associated with the relatively high frequency signals. Similarly, relatively low frequency signals that pass through low pass filter  668  are provided to signal power module  660   b  that estimates the signal power associated with the relatively low frequency signals. 
     A demodulated signal is provided to demodulator module  652  to a low pass filter  656 . In one embodiment, the cut-off frequency of low pass filter  656  may be approximately 500 Hz. The low frequency components of the demodulated signal which pass through low pass filter  656  are narrow band noise associated with the whiteboard microphone signal. Such a signal is generally associated with whiteboard erasing noise. The low frequency components of the demodulated signal, or the narrow band noise, are provided by low pass filter  656  to a signal power module  660   a  which estimates the signal power of the narrow band noise. 
     Signal powers are provided, by signal power modules  660   a - c , to noise floor estimation modules  664   a - c  and to an erasing noise detection module  672 . Noise floor estimation modules  664   a - c  may estimate the noise floor associated with the whiteboard microphone signal, and may be slow-up or fast-down low pass filters. The signal powers and the noise floor estimations may be used by erasing noise detection module  672  to estimate an envelope associated with erasing noise and to detect erasing noise. 
       FIG. 7  is a diagrammatic representation of an erasing noise detection system, e.g., erasing noise detection module  672  of  FIG. 6 , in accordance with an embodiment. Erasing noise detection module  672  obtains a low frequency signal power  776   b  and a low frequency signal noise floor  778   b  associated with a whiteboard microphone signal, e.g., from signal power module  660   b  and noise floor estimation module  664   b , respectively, of  FIG. 6 . Low frequency signal power  776   b  and low frequency signal noise floor  778   b  are processed, as for example by subtracting low frequency signal noise floor  778   b  from low frequency signal power  776   b , and compared to a corresponding threshold by a threshold comparison module  780   b . Erasing noise detection module  672  obtains a high frequency signal power  776   c  and a low frequency signal noise floor  778   c  associated with a whiteboard microphone signal, e.g., from signal power module  660   c  and noise floor estimation module  664   c , respectively, of  FIG. 6 . High frequency signal power  776   c  and low frequency signal noise floor  778   c  are processed and compared to a corresponding threshold by a threshold comparison module  780   c . A high frequency signal power  776   c  and a low frequency signal noise floor  778   c  associated with a whiteboard microphone signal, e.g., from signal power module  660   c  and noise floor estimation module  664   c , respectively, of  FIG. 6 . A narrow band signal power  776   a  and a narrow band signal noise floor  778   a  are obtained by erasing noise detection module  672 , and are processed and then compared to a corresponding threshold by a threshold comparison module  780   a.    
     In one embodiment, if threshold comparison module  780   b  determines that low frequency signal power  776   b  is lower than its corresponding threshold, if threshold comparison module  780   c  determines that high frequency signal power  776   c  is also lower than its corresponding threshold, and if threshold comparison module  780   a  determines that narrow band signal power  776   a  is above its associated threshold, then an envelope associated with narrow band signal power  776   a  is estimated by erasing noise envelope estimation module  782  to be an erasing noise envelope. A duration of the erasing noise envelope is effectively checked by erasing noise duration check module  784   a , e.g., compared to predefined thresholds. An erasing noise interval count, or a number of erasing noise periods detected in a predetermined time interval, is effectively identified by erasing noise interval count module  784   b . In one embodiment, an erasing noise period may be substantially defined as a period of erasing noise from a bottom to a peak to a bottom, and may generally be associated with a single swipe of an eraser on a surface of a whiteboard. 
     Erasing noise detection module  784  uses information provided by erasing noise duration check module  784   a  and erasing noise interval count module  784  to determine whether erasing noise is detected. In general, if the duration of the erasing noise envelope is determined by erasing noise detection module  784   c  as being too long or too short, the indication is that there is no erasing noise. The thresholds associated with determining whether the duration of an erasing noise envelope is too long or too short may vary widely. For example, an erasing noise envelope that has a duration of more than approximately 600 milliseconds (ms) may be considered to be too long, while an erasing noise envelope that has a duration of less than approximately 100 ms may be considered to be too short. If the erasing noise envelope indicates erasing noise, then erasing noise detection module  784   c  processes the erasing noise interval count obtained from erasing noise interval count module  784   b . If erasing noise detection module  784   c  determines that the erasing noise interval count is higher than a predetermined time interval, then erasing noise is detected. 
       FIG. 8  is a diagrammatic representation of a control system arranged to demodulate a signal, e.g., demodulation module  652  of  FIG. 6 , in accordance with an embodiment. Demodulation module  652  is configured to obtain a whiteboard microphone signal, and to demodulate the whiteboard microphone signal at a frequency fc. As previously mentioned, frequency fc may be approximately four kHz. As will be appreciated by those skilled in the art, demodulation module  652  may be a demodulator that effectively multiplies an input signal with a sine signal and a cosine signal with a certain frequency fc. 
     As previously mentioned, writing noise includes substantially any noise associated with a writing instrument such as a marker contacting a surface of a whiteboard, as for example when writing characters and/or drawing diagrams. Writing noise detection may involve, in one embodiment, detecting a click noise pulse followed by narrowband noise. A click noise pulse generally occurs when a writing instrument taps on a whiteboard, e.g., when the writing instrument first contacts the whiteboard at the beginning of a writing stroke. Narrowband noise generally occurs when a writing instrument slides across a whiteboard. 
     Referring next to  FIG. 9 , a system which processes a signal from a whiteboard microphone to determine if writing noise is detected will be described in accordance with an embodiment. A whiteboard microphone signal is provided to demodulation module  986   a ,  986   b , and to a signal power module  986   a  that is configured to estimate a signal power for the whiteboard microphone signal. The estimated signal power for the whiteboard microphone signal used by a noise floor determination module  992  that determines a noise floor. 
     Demodulation module  986   a  is arranged to demodulate the whiteboard microphone signal with a frequency fc 2  for sliding noise or narrowband noise detection, while demodulation module  986   b  is arranged to demodulate the whiteboard microphone signal with a frequency fc 3  for click noise detection. Although frequency fc 2  and frequency fc 1  may vary widely, in one embodiment, frequency fc 2  may be approximately four KHz and frequency fc 3  may be in a range between approximately 500 Hz and approximately 800 Hz. As will be appreciated by those skilled in the art, demodulating the whiteboard microphone signal may effectively reduce the likelihood that other noises are mistakenly identified as click noise or sliding noise. For example, demodulating the whiteboard microphone signal using demodulation module  986   b  may essentially prevent other pulse noises with different frequencies from being identified as click noise or as a click noise pulse which may have a frequency in the range of between approximately 500 Hz and approximately 800 Hz. 
     Once a signal is demodulated by demodulation module  986   a , the demodulated signal is provided to a low pass filter  990 . In the described embodiment, low pass filter  990  may have a cut-off frequency of approximately 500 Hz, although it should be understood that the cut-off frequency may vary. The signal power of the filtered, demodulated signal outputted by low pass filter  990  is estimated by a signal power module  988   b . The signal power estimated by signal power determination module  988   a , the noise floor determined by noise floor determination module  992 , and the signal power estimated by signal power determination module  988   b  are provided to a sliding noise estimation module  996   a  that is configured to detect the sliding of a writing instrument on a surface of a whiteboard. In one embodiment, sliding noise is effectively detected by sliding noise estimation module  996   a  when a ratio of signal power estimated by signal power module  988   b  to signal power estimated by signal power module  988   a  is above a predefined threshold, and the signal power estimated by signal power module  988   b  is above the noise floor determined by noise floor determination module  992 . 
     A signal demodulated by demodulation module  986   b  is provided to a short term signal power module  988   c , which estimates a short term signal power accumulation. A long term signal power accumulation is estimated by a long term signal power module  988   d , and a signal power envelope is determined by a signal power envelope determination module  994 . The long term signal power accumulation is used by a noise floor estimation module  996   b  to estimate a noise floor. 
     The long term signal power accumulation and the short term signal power accumulation, as well as the estimated noise floor, are compared by a pulse noise detection module  995  to predefined thresholds to determine when click noise is detected. The output of pulse noise detection module  995  is provided to a pulse count module  997 , to determine a number of pulse counts. In one embodiment, pulse noise detection module  995  detects a click noise pulse, and pulse count module  997  effectively counts how many clock noises are detected in a certain amount of time, e.g., in approximately two seconds. A click noise pulse may be detected if a short term signal power accumulation is approximately two ms and a long term signal power accumulation is approximately ten ms, and a duration of the pulse noise is approximately 60 ms or less. For example, a click noise pulse may reach its peak power within approximately two ms, and maintain a peak power for approximately four to five ms before the power decreases. 
     An output of sliding noise estimation module  996   a  and an output of pulse count module  997  are provided to a writing noise detection module  998 . Writing noise detection module  998  compares durations between consecutive click noise pulses between predetermined thresholds. By way of example, when a number of click noise pulses detected in a certain amount of time is above a threshold, writing noise detection module  998  may detect writing noise. When the duration between consecutive click noise pulses is determined to be too short, e.g., less than a corresponding predetermined threshold, or too high, e.g., more than a corresponding predetermined threshold, it is determined that the click noise pulses are invalid. By way of example, if consecutive click noise pulses are spaced apart by less than approximately 150 ms or are spaced apart by more than approximately one second, then the click noise pulses may be determined not to be indicative of writing noise. Writing noise detection module  998  may also determine if a number of click noise pulses followed by sliding noses is above a particular predetermined threshold during a predetermined time interval, then writing noise is indicated. For instance, during a predefined time interval of approximately two seconds, if a number of click noise pulses followed by pen sliding noises is above a predetermined threshold, e.g., five, then writing noise may be detected. As will be appreciated by those skilled in the art, the various predetermined thresholds used to determine whether writing noise is detected may vary widely. 
       FIG. 10  is a block diagram representation of a server configured to determine when there is activity on a whiteboard and to control a camera to focus on the whiteboard when there is activity on the whiteboard in accordance with an embodiment. A server  1002 , which may be a conference server or a server that supports a collaboration session, generally includes an input/output (I/O) interface  1006 , a processing arrangement  1010 , and hardware and/or software logic  1014 . As will be appreciated by those skilled in the art, server  1002  is generally part of an overall collaboration system that includes at least one camera (not shown) and a plurality of microphones (not shown), as well as a whiteboard (not shown). 
     I/O interface  1006 , which may include at least one port (not shown) arranged to allow server  1002  to obtain information from a network and to provide information on the network. In general, I/O interface  1006  may obtain sound information from microphones (not shown) that allows server  1002  to determine whether activity on a whiteboard (not shown) is detected. 
     Processing arrangement  1010 , which may include one or more processors (not shown), is arranged to execute software logic included in logic  1014 . Logic  1014  includes a camera control arrangement  1018   a , a sound processing arrangement  1018 , and a conference bridge arrangement  1018   c . Camera control arrangement  1018   a  is configured to position a camera (not shown), e.g., to focus on a whiteboard (not shown) when activity is detected on the whiteboard. Sound processing arrangement  1018   b  is configured to utilize data obtained through I/O interface  1006  to identify activity on a whiteboard (not shown). Sound processing arrangement  1018   b  includes an erasing noise detection module  1022   a  arranged to determine when data obtained through I/O interface  1006  indicates that erasing activity is likely occurring, and a writing noise detection module  1022   b  arranged to determine when data obtained through I/O interface  1006  indicates that writing activity is likely occurring. Conference bridge arrangement  1018   c  is configured to support a conference bridge or, more generally, a collaboration session. 
     Although only a few embodiments have been described in this disclosure, it should be understood that the disclosure may be embodied in many other specific forms without departing from the spirit or the scope of the present disclosure. By way of example, control systems used to effectively identify whether noise activity is detected by a whiteboard microphone and, further, whether detected noise activity is erasing noise or writing noise, may vary widely. 
     The thresholds set for low pass filters and/or high pass filters may vary widely without departing from the spirit or the scope of the present disclosure. In other words, cut-off frequencies for low pass filters and for high pass filters may vary depending upon the requirements of a particular system. In addition, the various thresholds used to determine whether erasing noise is detected and whether writing noise is detected may also vary depending upon the requirements of a particular system. 
     In some instances, activity at a whiteboard may include both writing and erasing at substantially the same time. For example, an individual using a whiteboard may alternate between writing and erasing. A method of detecting activity on a whiteboard may be augmented to include identifying alternating time periods during which writing noise is detected and time periods during which erasing noise is detected. 
     While the functionality associated with determining when activity has been detected on a surface of a whiteboard, or when noise activity indicates that a whiteboard is likely being used by a presenter, has been described as being embodied on a server, it should be appreciated that such functionality is not limited to being embodied on a server. By way of example, functionality that allows a determination to be made as to when a whiteboard is likely being used may be distributed throughout a network such that parts of the functionality are embodied on different elements or nodes within the network 
     The embodiments may be implemented as hardware, firmware, and/or software logic embodied in a tangible, i.e., non-transitory, medium that, when executed, is operable to perform the various methods and processes described above. That is, the logic may be embodied as physical arrangements, modules, or components. A tangible medium may be substantially any computer-readable medium that is capable of storing logic or computer program code which may be executed, e.g., by a processor or an overall computing system, to perform methods and functions associated with the embodiments. Such computer-readable mediums may include, but are not limited to including, physical storage and/or memory devices. Executable logic may include, but is not limited to including, code devices, computer program code, and/or executable computer commands or instructions. 
     It should be appreciated that a computer-readable medium, or a machine-readable medium, may include transitory embodiments and/or non-transitory embodiments, e.g., signals or signals embodied in carrier waves. That is, a computer-readable medium may be associated with non-transitory tangible media and transitory propagating signals. 
     The steps associated with the methods of the present disclosure may vary widely. Steps may be added, removed, altered, combined, and reordered without departing from the spirit of the scope of the present disclosure. Therefore, the present examples are to be considered as illustrative and not restrictive, and the examples is not to be limited to the details given herein, but may be modified within the scope of the appended claims.