, a state machine module 230, and a content database 240 In other embodiments, the audio system 200 may comprise additional components, fewer components, different components, or some combination thereof. For example, the audio system 200 may comprise components such as one or more acoustic sensors, loudspeakers (not shown), or some combination thereof. In other embodiments, the various functions described as performable by the components may be variably distributed between the components.

The notch filter 210 is a signal filter that is designed to remove one or more frequencies from a signal. The notch filter may also be referred to as a band stop filter or band reject filter. A notch refers to a set of frequencies, for example, a frequency range that may be blocked by a notch filter 210. The notch filter 210 rejects (i.e., blocks) or attenuates signals in a specific frequency range called the stop band frequency range and passes the signals above and below this frequency range. According to an embodiment, the notch filter is an adaptive notch filter that is a variable notch filter such that the notch frequency of the notch filter is adaptively controlled in real time. According to an embodiment, the notch filter is a second order adaptive notch filter that has two poles and two zeros. The notch filter is adaptive since the system dynamically determines the frequencies where the howl occurs so that those frequencies can be suppressed.

The tonality detection module 220 monitors tonality of the audio signal processed to identify tones appearing in the signal. According to an embodiment, the tonality detection module 220 performs tonality detection using linear prediction.

The state machine module 230 manages a state machine that suppresses the howl detected in a device. The details of the state machine are shown in FIG. 4 and described in connection with FIG. 4.

FIG. 3 shows the overall process flow illustrating how the system manages howl according to an embodiment. The system monitors 310 tonality of the signal processed to identify tones appearing in the signal. A tone refers to a sound composed of a single frequency component. Tonality is a metric representing the relative weight of various tonal components in a signal. The signal may include tones that are not howls. The howl may occur due to instability and may occur at multiple frequencies where instability occurs. At theses frequencies, the level of the signal becomes very high, i.e., above a threshold value and is considered a howl. Accordingly, the system monitors 320 the signal levels to determine whether the signal level for particular tones exceeds a threshold value indicating occurrence of howl. The system combines the information monitored in the tonality and signal levels and provides 330 the combined information to a state machine. The system uses the state machine to manage 340 the howl.

FIG. 4 illustrates the state machine used by the system to manage howl according to an embodiment. The state 410a represents a state where there is no howl and the device is working properly without howl. The system increases (i.e., ramps up) the signal to provide better sound and better user experience to the user via the device. The system may continue increasing the gain to reach a maximum gain value. The system may detect howl and reach the state 410b. In response to detecting howl, the system decreases (i.e., ramps down) the gain in state 410b. The system attempts to stabilize the system by reducing the gain in an attempt to eliminate the howl. The system keeps the reduced gain for at least a threshold time in state 410c and waits for the howl to get eliminated. Once the howl disappears, the system may return to state 410a and again start increasing the gain.

FIG. 5 illustrates a second order adaptive notch filter with two poles and two zeros, according to an embodiment. As shown in FIG. 5, the poles 510a, 510b are represented using circles and the zeros 520a, 52b are represented using Xs. The poles are on the unit circle but the zeros are closer to the origin than the poles. The radius r shown in FIG. 5 is the distance of the zeros from the origin. The radius r is less than the radius of unit circles to avoid the zeros being too close to the poles which avoids instability. The angle θ determines where the notch is placed in the frequency domain to block or attenuate a cluster of frequencies that represent a howl.

The following equation (1) represents a two-pole notch filter according to an embodiment.

$\begin{matrix} H (z) = \frac{(1 - e^{j θ} z^{- 1}) (1 - e^{- j θ} z^{- 1})}{(1 - {re}^{j θ} z^{- 1}) (1 - {re}^{- j θ} z^{- 1})} & (1) \end{matrix}$

In the equation (1) the two terms in the numerator represent the zeros of the notch filter and the two terms in the denominator represent the poles of the notch filter. The equation (1) can be simplified into equation (2). In equation 2, the term cos(0) represents the location of the notch in frequency domain and r represents the depth of the notch.

$\begin{matrix} H (z) = \frac{1 - 2 \cos (θ) z^{- 1} + z^{- 2}}{1 - 2 r \cos (θ) z^{- 1} + r^{2} z^{- 2}} & (2) \end{matrix}$

The system determines the location of the notch, i.e., the set or range of frequencies that where the howl is present and that need to be blocked. According to an embodiment, the system uses linear prediction to determine the location of the notch filter. The system uses linear prediction by determining the location of the notch filter using a linear function of samples. According to an embodiment, the system determines whether a howl is present based on variance of the predictive estimates of the linear predictor.

FIG. 6 illustrates an adaptive graph based on terms of the notch filter, according to an embodiment. For example, FIG. 6 represents the denominator of the equation (2), i.e., the expression 1-2r cos(θ)z⁻¹+r²z⁻². The adaptive graph receives an input signal 610. The term z⁻²represents a second order delay. The input delayed by second order delay is multiplied by the term r²representing a tuning parameter. The term z⁻¹represents a first order delay. The input delayed by first order delay is multiplied by the term r representing a tuning parameter and further multiplied by the term cos(θ) representing the location of the notch. The system runs the adaptive graph shown in FIG. 6. When the system converges, the term w=cos(θ) is determined, thereby providing the frequency of the howl. To extract the frequency of the howl, from the value of the cos(θ) the system may use the equation (3). Accordingly, the system determines the inverse cosine of the value w, divides the result by π and multiplies it by the term f_s/2, where f_srepresents the sampling frequency. The result is the frequency f_hzthat represents the location of the howl.

$\begin{matrix} f_{hz} = \frac{\cos^{- 1} (w)}{π} \cdot \frac{f_{s}}{2} & (3) \end{matrix}$

FIG. 8 illustrates a process for determining howl frequency by measuring flatness of the signal, according to an embodiment. The system receives 810 audio signal that is processed by the device that includes both speaker(s) and microphone(s). The system determines 820 a metric representing flatness of the signal. FIG. 7 illustrates monitoring of flatness of the signal to determine the howl frequencies according to an embodiment. As shown in FIG. 7, the signal is flat, i.e., the signal 720 is within a short range of frequencies when the howl is present as compared to the signal 710 when no howl is present. Accordingly, the signal when howl is present is more stable compared to the signal without the howl. The signal 710 when no howl is present has a wider range of frequencies compared to the signal 720. During certain time interval the system may detect 830 howl based on the flatness metric, for example, if the system determines that the flatness of the signal suddenly increases by more than a threshold value. The system may run the state machine illustrated in FIG. 4 to determine 840 the howl frequency accurately. For example, the system may increase the gain of the signal at particular frequencies to determine whether howl is generated. If the howl is generated by a particular frequency, the system selects that frequency as the howl frequency. Once the system determines the howl frequency, the system configures 850 the notch filter based on the frequency of howl. The system applies 860 the notch filter to the signal to remove the howl.

The howl frequency determined by the system using the adaptive notch filter prediction may not represent the actual frequency at which the howl occurs. For example, the howl may occur at two frequencies f1 and f2 and the adaptive notch filter may predict that the howl is at a frequency f3 that is an average of the two frequencies f1 and f2 and is different from both f1 and f2. The system monitors whether the adaptive notch filter is consistently predicting the same frequency f3. The system uses this prediction as indicating that howl is occurring in the system which many be at a frequency different from the frequency f3 predicted using the adaptive notch filter prediction. If the howl occurs at one frequency, the adaptive notch filter accurately predicts the howl frequency. In this embodiment, the system uses the adaptive notch filter to not eliminate the howl but just to detect the presence of howl. The system eliminates the howl by reducing the gain for all frequencies of the signal as indicated in state 410b of FIG. 4. By reducing the gain, the system eliminates the howl.

According to an embodiment, the system divides the frequencies into a plurality of frequency ranges and an adaptive notch filter is assigned to each frequency range. Accordingly, if the notch filter assigned to a particular frequency range indicates detection of howl, the system determines that the howl frequency is within that frequency range. Accordingly, the system reduces gain of the signal in all the frequencies within that frequency range to eliminate the howl. In this embodiment, the gain of the signal in remaining frequency ranges is not reduced and the user can still hear the signal.

According to an embodiment, the system monitors gain using a first order smoothing based level detector that detects the level of then signal and performs smoothing of the level. A system may use a coherence function to perform tonality detection in sub-band domain that can also detect the howl for suppressing the howl. The system determines a coherence between the signal and a delayed signal. The coherence is an indication of tonal components of the signal including howl. A coherence function-based detection of tonality may be very noisy thereby creating difficulty in placement of thresholds.

The system according to an embodiment uses linear prediction instead of coherence function. The linear prediction may be expressed using the following equations.

$\begin{matrix} E (k, τ) = Y (k, τ) - C_{y} (k, τ - 1) \cdot Y (k, τ - 1) & (4) \end{matrix}$

$\begin{matrix} Ψ (k, τ) = Ψ (k, τ) \cdot (1 - α) + α \cdot {❘ Y (k, τ - 1) ❘}^{2} & (5) \end{matrix}$

$\begin{matrix} μ (k, τ) = \frac{μ_{0}}{Ψ_{k, τ} + δ} & (6) \end{matrix}$

$\begin{matrix} C_{y} (k, τ) = A_{leak} \cdot C_{y} (k, τ - 1) + μ (k, τ) \cdot E (k, τ) \cdot Y^{*} (k, τ - 1) & (7) \end{matrix}$

The variables used in the equation are as follows.

- τ→block-time index
- k→subband-frequency index
- E(k,τ)→Error signal
- Y(k,τ)→input signal
- Y(k,τ−1)→delayed input signal
- C_y(k,τ)→linear prediction weight
- α→smoothing constant
- Ψ(k,τ)→excitation energy
- μ(k,τ)→step-size
- A_leak→leakage parameter

Accordingly, the system determines an error signal E(k,τ) based on equation (4). The system further determines an excitation energy Ψ(k,τ) using equation (5). The system determines the step size μ(k,τ) using equation (6). The system determines a linear prediction weight C_y(k,τ) using equation (7).

FIG. 9 shows a graph illustrating tonality detection in a sub-band using linear prediction in frequency domain according to an embodiment. The system receives a sub-band input 910 for a particular band of frequencies. The sub-band input is delayed 820. The system estimates a complex weight 940 that undergoes a leakage 930 by a term alpha. The complex weight C_y(k,τ) is determined using the equations (4), (5), (6), and (7). The system uses the complex weight as an indication of tonality including the howl. The tonality detection based on linear prediction is significantly less noisy compared to a coherence-based tonality detection, for example, when there are no howls or significant tones.

Example System Environment

FIG. 10 is a system 1000 that includes a headset 1005, in accordance with one or more embodiments. In some embodiments, the headset 1005 may be the headset 100 of FIG. 1A or the headset 105 of FIG. 1B. The system 1000 may operate in an artificial reality environment (e.g., a virtual reality environment, an augmented reality environment, a mixed reality environment, or some combination thereof). The system 1000 shown by FIG. 10 includes the headset 1005, an input/output (I/O) interface 1010 that is coupled to a console 1015, the network 1020, and the mapping server 1025. While FIG. 10 shows an example system 1000 including one headset 1005 and one I/O interface 1010, in other embodiments any number of these components may be included in the system 1000. For example, there may be multiple headsets each having an associated I/O interface 1010, with each headset and I/O interface 1010 communicating with the console 1015. In alternative configurations, different and/or additional components may be included in the system 1000. Additionally, functionality described in conjunction with one or more of the components shown in FIG. 10 may be distributed among the components in a different manner than described in conjunction with FIG. 10 in some embodiments. For example, some or all of the functionality of the console 1015 may be provided by the headset 1005.

The headset 1005 includes the display assembly 1030, an optics block 1035, one or more position sensors 1040, the DCA 1045, and the audio system 200. Some embodiments of headset 1005 have different components than those described in conjunction with FIG. 10. Additionally, the functionality provided by various components described in conjunction with FIG. 10 may be differently distributed among the components of the headset 1005 in other embodiments, or be captured in separate assemblies remote from the headset 1005.

The display assembly 1030 displays content to the user in accordance with data received from the console 1015. The display assembly 1030 displays the content using one or more display elements (e.g., the display elements 120). A display element may be, e.g., an electronic display. In various embodiments, the display assembly 1030 comprises a single display element or multiple display elements (e.g., a display for each eye of a user). Examples of an electronic display include: a liquid crystal display (LCD), an organic light emitting diode (OLED) display, an active-matrix organic light-emitting diode display (AMOLED), a waveguide display, some other display, or some combination thereof. Note in some embodiments, the display element 120 may also include some or all of the functionality of the optics block 1035.

The optics block 1035 may magnify image light received from the electronic display, corrects optical errors associated with the image light, and presents the corrected image light to one or both eyeboxes of the headset 1005. In various embodiments, the optics block 1035 includes one or more optical elements. Example optical elements included in the optics block 1035 include: an aperture, a Fresnel lens, a convex lens, a concave lens, a filter, a reflecting surface, or any other suitable optical element that affects image light. Moreover, the optics block 1035 may include combinations of different optical elements. In some embodiments, one or more of the optical elements in the optics block 1035 may have one or more coatings, such as partially reflective or anti-reflective coatings.

Magnification and focusing of the image light by the optics block 1035 allows the electronic display to be physically smaller, weigh less, and consume less power than larger displays. Additionally, magnification may increase the field of view of the content presented by the electronic display. For example, the field of view of the displayed content is such that the displayed content is presented using almost all (e.g., approximately 110 degrees diagonal), and in some cases, all of the user's field of view. Additionally, in some embodiments, the amount of magnification may be adjusted by adding or removing optical elements.

In some embodiments, the optics block 1035 may be designed to correct one or more types of optical error. Examples of optical error include barrel or pincushion distortion, longitudinal chromatic aberrations, or transverse chromatic aberrations. Other types of optical errors may further include spherical aberrations, chromatic aberrations, or errors due to the lens field curvature, astigmatisms, or any other type of optical error. In some embodiments, content provided to the electronic display for display is pre-distorted, and the optics block 1035 corrects the distortion when it receives image light from the electronic display generated based on the content.

The position sensor 1040 is an electronic device that generates data indicating a position of the headset 1005. The position sensor 1040 generates one or more measurement signals in response to motion of the headset 1005. The position sensor 190 is an embodiment of the position sensor 1040. Examples of a position sensor 1040 include: one or more IMUs, one or more accelerometers, one or more gyroscopes, one or more magnetometers, another suitable type of sensor that detects motion, or some combination thereof. The position sensor 1040 may include multiple accelerometers to measure translational motion (forward/back, up/down, left/right) and multiple gyroscopes to measure rotational motion (e.g., pitch, yaw, roll). In some embodiments, an IMU rapidly samples the measurement signals and calculates the estimated position of the headset 1005 from the sampled data. For example, the IMU integrates the measurement signals received from the accelerometers over time to estimate a velocity vector and integrates the velocity vector over time to determine an estimated position of a reference point on the headset 1005. The reference point is a point that may be used to describe the position of the headset 1005. While the reference point may generally be defined as a point in space, however, in practice the reference point is defined as a point within the headset 1005.

The DCA 1045 generates depth information for a portion of the local area. The DCA includes one or more imaging devices and a DCA controller. The DCA 1045 may also include an illuminator. Operation and structure of the DCA 1045 is described above with regard to FIG. 1A.

The I/O interface 1010 is a device that allows a user to send action requests and receive responses from the console