EP4365890A1 - Appareil et procédé de génération adaptative de sons de masquage vocaux harmoniques - Google Patents

Appareil et procédé de génération adaptative de sons de masquage vocaux harmoniques Download PDF

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Publication number
EP4365890A1
EP4365890A1 EP22205855.4A EP22205855A EP4365890A1 EP 4365890 A1 EP4365890 A1 EP 4365890A1 EP 22205855 A EP22205855 A EP 22205855A EP 4365890 A1 EP4365890 A1 EP 4365890A1
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Prior art keywords
signal
limited
masking
frequency band
frequency
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English (en)
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Benjamin MÜLLER
David STROBEL
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Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung eV
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Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung eV
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Priority to EP22205855.4A priority Critical patent/EP4365890A1/fr
Priority to PCT/EP2023/080738 priority patent/WO2024099913A1/fr
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/1752—Masking
    • G10K11/1754—Speech masking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K3/00—Jamming of communication; Counter-measures
    • H04K3/40—Jamming having variable characteristics
    • H04K3/42—Jamming having variable characteristics characterized by the control of the jamming frequency or wavelength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K3/00—Jamming of communication; Counter-measures
    • H04K3/40—Jamming having variable characteristics
    • H04K3/43—Jamming having variable characteristics characterized by the control of the jamming power, signal-to-noise ratio or geographic coverage area
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K3/00—Jamming of communication; Counter-measures
    • H04K3/40—Jamming having variable characteristics
    • H04K3/45—Jamming having variable characteristics characterized by including monitoring of the target or target signal, e.g. in reactive jammers or follower jammers for example by means of an alternation of jamming phases and monitoring phases, called "look-through mode"
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K3/00—Jamming of communication; Counter-measures
    • H04K3/80—Jamming or countermeasure characterized by its function
    • H04K3/82—Jamming or countermeasure characterized by its function related to preventing surveillance, interception or detection
    • H04K3/825—Jamming or countermeasure characterized by its function related to preventing surveillance, interception or detection by jamming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K2203/00—Jamming of communication; Countermeasures
    • H04K2203/10—Jamming or countermeasure used for a particular application
    • H04K2203/12—Jamming or countermeasure used for a particular application for acoustic communication

Definitions

  • the application relates to noise masking, in particular speech masking, and, in particular, to an apparatus and a method for adaptive, harmonic speech masking sound generation.
  • Requirements for an acoustic workplace environment vary both over the course of the working day and with the different tasks that employees have to perform. For example, people who work in a crowded office have a high need for acoustic privacy, while people who work in a sparsely occupied office may need an expanded hearing horizon, for example, in order not to be surprised by the sudden appearance of other people (Zuydervliet et al., 2008).
  • Some approaches are based on dynamically adjusting the volume of masking sounds to changing background noise conditions. For example, there are global system approaches in which the masking sound changes at fixed time intervals or based on microphone measurements throughout the office. These offer a rather inflexible and therefore inadequate solution. In addition, employee satisfaction increases when employees have the opportunity to personalize their workplace (see Huang, Robertson & Chang, 2004; Lee & Brand, 2010).
  • Chanaud (2007) presented two systems of adaptive sound masking.
  • a time-based system in which the sound pressure level of the masking sound varies in static time intervals throughout the day. For this, different needs for acoustic privacy and the expected level of noise intensity must be predicted for different times of day. For example, it is important for employees to be able to hear the presence of other people overnight and in the early morning. Accordingly, no or only very quiet masking sound would be sufficient at this time.
  • the 10th percentile L10 of the measured sound level describes the sound level that was reached in at least 10% of the part-time period considered.
  • the 90th percentile L90 describes the sound level that was reached in at least 90% of the part-time period considered.
  • Zuydervliet et al. (2008) also suggests that the 10th and 90th percentile values should be determined for adaptive masking sound control.
  • the 90th percentile L AF,90% represents the background noise of the ambient sound and the 10th percentile L AF,10% describes the activity transients of disturbing sounds in the background noise condition.
  • the difference between these L AF,10%-90% values therefore describes an SNR of disturbing components and background noise (Zuydervliet et al., 2008). If the SNR is high, the background sound has a large changing state character and therefore causes an ISE.
  • Zuydervliet et al. (2008) with a target value L AF,10%-90%,target , i.e. with an optimal percentile value difference.
  • the masking sound level should slowly increase and thus reduce the signal-to-noise ratio (SNR) of the total sound. If the difference is smaller, the SNR of the sum signal is already smaller than the minimum necessary to not cause an ISE, and the masking sound can slowly become quieter.
  • Other parameters such as a weighting factor W, a maximum volume change per minute, and a parameter for adjusting the sensitivity can thus influence the optimal volume of the masking sound (Zuydervliet et al., 2008).
  • the target value (L AF,10%-90%,target ) should be between 3 and 10 dB, while the weighting factor should be between 0.5 and 4.
  • the time period over which the analysis of the percentile values takes place determines the sensitivity of the system, whereby a period of 15 s is suggested. If a longer period is selected, level fluctuations are less significant and the control system reacts more slowly to changing sound conditions. A value of 0.05 dB per second is given as the maximum rate of change (L'Esperance et al., 2017).
  • the level increase should generally be faster than the level reduction of the masking sound. It is also suggested that the upper and lower limits of adaptive masking sound systems should be limited. This should ensure that sufficient masking is ensured at all times, but at the same time a maximum acceptable level is not exceeded. Zuydervliet et al. (2008) suggests a dynamic range of 5 dB for the masking sound volume, whereas the work by L'Esoutheasternrance (2017) suggests 3 dB.
  • Renz (2019) also focuses on percentile value differences L AF,10% - L AF,90% and has developed a new method for predicting an expected decrease in performance DP. Renz (2019) suggests 2 to 3 dB as a suitable target value (L AF,10%-90%,target ) for adaptive level control of masking sounds.
  • Renz The DP values evaluated and plotted by Renz (2019), "Personalised sound masking in open offices. A trade-off between annoyance and restoration of working memory performance?" Stuttgart: Fraunhofer Verl, Stuttgart, as a function of the prediction parameter L AF,10%-90% are shown in Renz, 2019 on page 204. Renz, 2019, shows on page 204 a plot of the cognitive performance prediction model of the resulting DP with the prediction parameter LAF 10-90.
  • US 2003/103632 A1 presents an adaptive noise masking system and noise masking method that divides unwanted noise into time blocks and estimates the frequency spectrum and power level, while continuously generating white noise with an appropriate spectrum and power level to mask the unwanted noise.
  • CN110362789A shows a noise masking method and an adaptive noise masking system with a noise masking database, a noise satisfaction agent model and a self-adaptive noise masking search system.
  • US 2015/194144 A1 shows a multi-microphone subsystem to capture sounds, a spectrum analyzer to determine a performance characteristic of the captured sound and a spatial analyzer to detect a directional characteristic of the sound.
  • a device for generating speech masking sound comprises an analyzer for analyzing each frequency band-limited signal portion of a plurality of frequency band-limited signal portions of a microphone signal during an analyzed time period to obtain information about the frequency band-limited signal portion. Furthermore, the device comprises a masking signal generator for generating a masking signal depending on the information about the frequency band-limited signal portion of each of the plurality of frequency band-limited signal portions. The information about the frequency band-limited signal portion depends on a first sound level that was reached at least during a first time period during the analyzed time period. Furthermore, the information about the frequency band-limited signal portion depends on a second sound level that was reached at least during a second time period during the analyzed time period, wherein the second time period is different from the first time period.
  • the information on the frequency band-limited signal component depends on a first sound level which is present at least during a first period of time during the analyzed period. Furthermore, the information on the frequency band-limited signal component depends on a second sound level that was reached at least during a second time period during the analyzed period, the second time period being different from the first time period.
  • Embodiments provide a control algorithm that can adjust a speech masking signal for presentation over headphones in a way that is both comfortable and secure and psychoacoustically validated.
  • Some embodiments provide a masking sound, which can be individually adjusted via headphones, for example, and is only used to the extent that it is needed. This creates an effective way to improve both the cognitive performance and the satisfaction of employees in the workplace.
  • Fig.1 shows a device for generating speech masking sound according to an embodiment.
  • the device comprises an analyzer 110 for analyzing each frequency band-limited signal component of a plurality of frequency band-limited signal components of a microphone signal during an analyzed period of time in order to obtain information about the frequency band-limited signal component.
  • the device comprises a masking signal generator 120 for generating a masking signal depending on the information about the frequency band-limited signal component of each of the plurality of frequency band-limited signal components.
  • the information about the frequency band-limited signal component depends on a first sound level that was reached at least during a first time period during the analyzed time period. Furthermore, the information about the frequency band-limited signal component depends on a second sound level that was reached at least during a second time period during the analyzed time period, the second time period being different from the first time period.
  • the analyzer 110 can be designed, for example, to determine a microphone signal sound level difference between the first sound level and the second sound level for each signal component of the plurality of frequency-band-limited signal components of the microphone signal.
  • the masking signal generator 120 can be designed, for example, to determine the masking signal depending on the microphone signal sound level difference of each frequency-band-limited signal component of the plurality of frequency-band-limited signal components of the microphone signal.
  • the masking signal generator 120 can be designed, for example, to determine the masking signal by determining, for each signal component of the plurality of frequency-band-limited signal components, a level value for a frequency-band-limited component of the masking signal that corresponds to a frequency range of this signal component, depending on the microphone signal sound level difference of this signal component, and to carry out a level adjustment of this frequency-band-limited component of the masking signal using this level value.
  • the analyzer 110 can be designed, for example, to determine an overall signal that depends on the microphone signal but is different from the microphone signal.
  • the analyzer 110 can be designed, for example, to determine an error value for each frequency-band-limited signal component of the plurality of frequency-band-limited signal components of the overall signal, which error value indicates a difference between a target value for an overall signal sound level difference and a current overall signal sound level difference of the overall signal.
  • the masking signal generator 120 can be designed, for example, to determine the masking signal depending on the error value for each frequency-band-limited signal component of the plurality of frequency-band-limited signal components of the overall signal.
  • the analyzer 110 can, for example, be designed to determine the current total signal sound level difference between a third sound level and a fourth sound level for each frequency band-limited signal portion of the plurality of frequency band-limited signal portions of the total signal, wherein the third sound level is a sound level that was reached at least during a third time period during an analyzed time period in the frequency band-limited signal portion of the total signal, and wherein the fourth sound level is a sound level that was reached at least during a fourth time period during the analyzed time period in the frequency band-limited signal portion of the total signal.
  • the analyzer 110 can, for example, be designed to determine each frequency-band-limited signal component of the plurality of frequency-band-limited signal components of the overall signal depending on a feedback time section of the masking signal to this frequency-band-limited signal component.
  • the analyzer 110 can be designed, for example, to determine each of the plurality of frequency-band-limited signal components of the overall signal depending on an attenuation factor for this frequency-band-limited signal component of the overall signal, wherein the analyzer 110 is designed to apply the attenuation factor for this frequency-band-limited signal component to the corresponding frequency-band-limited signal component of the microphone in order to obtain an attenuated microphone signal for this frequency-band-limited signal component.
  • the analyzer 110 may be configured, for example, to analyze each frequency band-limited signal component of the plurality of frequency band-limited Signal components of the overall signal are to be determined as the sum of the fed-back time section of the masking signal to this frequency band-limited signal component and the attenuated microphone signal to this frequency band-limited signal component.
  • the masking signal generator 120 can, for example, be designed to determine the masking signal as a function of a correction value for each frequency-band-limited signal component of the plurality of frequency-band-limited signal components of the overall signal, wherein the masking signal generator 120 is designed to determine the correction value for this frequency-band-limited signal component as a function of the error value for this frequency-band-limited signal component.
  • the masking signal generator 120 can further be designed, for example, to determine the correction value for this frequency band-limited signal component depending on a temporal predecessor value of this correction value.
  • the masking signal generator 120 can, for example, be designed to determine the masking signal as a function of a control value for each frequency-band-limited signal component of the plurality of frequency-band-limited signal components of the microphone signal, wherein the masking signal generator 120 is designed to determine the control value for this frequency-band-limited signal component as a function of the microphone signal sound level difference of this frequency-band-limited signal component and as a function of the error value and the correction value of this frequency-band-limited signal component of the overall signal.
  • the masking signal generator 120 can be designed, for example, to determine the control value for this frequency band-limited signal component by forming a sum of the microphone signal sound level difference of this frequency band-limited signal component and the error value and the correction value of this frequency band-limited signal component of the total signal.
  • the masking signal generator 120 can, for example, be designed to determine the level value for a frequency band-limited component of the masking signal depending on the control value for this frequency band-limited signal component and depending on a previous level value for this frequency band-limited component of the masking signal.
  • Embodiments provide a control algorithm that enables a masking sound to be dynamically adapted in volume and in its frequency spectrum to a background sound condition.
  • the algorithm can, for example, independently detect the extent to which the background noise condition can have a disruptive influence on cognitive performance.
  • a microphone signal is used to assess the noise condition.
  • the algorithm works on different end devices with the technology available in each case. Since it cannot be assumed that all end devices have calibrated, standard-compliant microphones installed that meet the requirements for sound level meters according to DIN EN 61672-1, the algorithm does not require any knowledge of the absolute sound pressure level. The algorithm determines the 90% and 10% percentile values as control parameters.
  • a masking sound is generated and played, which continuously has a sufficient masking effect to prevent a possible ISE-related cognitive performance decline that can arise from the background noise condition.
  • the algorithm has an appropriate sensitivity to the background noise condition so that spontaneously occurring noises that are not representative of the background noise condition are not used for control.
  • the masking sound generated by the algorithm is only as loud as necessary at any given time.
  • the goal is not only to increase performance objectively, but also to ensure the acoustic satisfaction of the users.
  • Masking sounds in general are perceived as more unpleasant than silence.
  • the algorithm recognizes at any time which The system determines the minimum masking sound level that is currently required and uses this continuously as the target value for level control.
  • the ratio of the L AF,10% percentile value to the L AF,90% percentile value is used as the target value.
  • the masking sound adapts to the frequency spectrum of the background noise.
  • the control times with which the volume of the masking sound is controlled are selected by the algorithm so that the volume fluctuations are barely noticeable. This is useful so that the masking sound itself does not distract the user. At the same time, however, volume changes occur quickly enough to be able to react to changed acoustic conditions in the background noise condition.
  • the algorithm adds a harmonic component to the masking sound, which ensures a pleasant sound of the masking sound.
  • Fig.2 a signal flow diagram according to an embodiment with a frequency division into nine octave bands, which in the example of Fig.2 Center frequencies at 63 Hz, 125 Hz, 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz, 800 Hz, 1600 Hz.
  • Fig. 2 This illustrates Fig. 2 the part of the algorithm in which the frequency division of the microphone input signal into bands, for example into octave bands, takes place.
  • Fig. 2 to see how the various band-filtered masking sound components are mixed together and calculated with a calibration factor W before the masking signal is output to the headphones.
  • the light blue framed elements with the inscription "Adaptive Level Control" from Fig. 2 represent the part of the algorithm which is Fig.3 is illustrated in detail.
  • FIG.3 shows a signal flow diagram for adaptive noise masking according to an embodiment.
  • the level value measurement, the percentile value difference determination and the continuous calculation of the control value u take place. Furthermore, the control values u are smoothed here by set control times in order to obtain the level value p, which in turn controls the volume of the respective band-filtered masking sound component.
  • Fig.4 a signal flow diagram of a control value checker according to an embodiment.
  • Fig.4 shown how the percentile values of the level values of the entire signal (microphone signal * damping factor + (returned) masking sound component) are calculated.
  • This resulting calculated level difference (L AF,10%-90%,Total ) is compared with the target value to obtain an error value (e).
  • Fig.5 shows a control loop according to an embodiment. In particular, Fig.5 to see how a correction value is calculated from the error value e.
  • the input of the algorithm is as in Fig.2 shown is the digital audio signal of a microphone which continuously records ambient noise.
  • This signal is first divided into bands by octave filters in accordance with DIN EN 61260-1, e.g. into nine octave bands (e.g. with center frequencies in the range 63 Hz - 16 000 Hz), with the band-limited signals being analyzed and processed in the respective signal paths. Since the adaptive level control is to take place per band, the masking signal is also divided into individual bands. The volume of these band-filtered masking sounds is controlled individually in the signal paths (corresponding to the respective octave band) and then mixed together again to form an overall masking sound. This makes it possible to calculate the current interference influence for each octave band, which can be used to determine the volume control that the respective frequency range of the masking sound should have in order to sufficiently mask the interference sounds that occur across the entire frequency spectrum.
  • the controlled masking signal is supplemented with an additional harmonic component.
  • the harmonic component is a type of music that improves the acceptance and subjective perception of the masking sound.
  • the harmonic component is included in the calculation of the expected interference effect of the acoustic environment described below.
  • the harmonic component, mixed with the controlled masking component is played through the headphones.
  • the harmonic component is psychoacoustically secured, i.e. its suitability has been tested in listening tests (no changing state behavior, as the ISE is not triggered).
  • the harmonic component can be, for example, an uncompressed stereo file that is played and controlled by the algorithm.
  • the input of adaptive sound masking is the band-filtered and A-weighted audio signal from the microphone.
  • A-weighting is a commonly used frequency weighting that represents the ear's response to sound pressure or volume.
  • the weightings F (fast), S slow (slow) and I (impulse) indicate how quickly a reaction occurs to a change in the sound level.
  • LAF refers to a sound level with A-frequency weighting and F-time weighting.
  • the percentile levels L AF,10% and L AF,90% indicate which levels were reached in 10% and 90% of the measurement time, respectively.
  • the equivalent continuous sound level is now determined from the said audio signal in accordance with DIN EN 61672-1.
  • a root mean square (RMS) is first determined per sample.
  • the level values are then integrated over 125 milliseconds. In order to avoid any errors that may occur in further signal processing with unrealistically small amplitude values, the values are limited by a minimum amplitude value.
  • the measured level values are saved in a continuous list, with the list length defining the observation period over which the percentile values are analyzed. Due to the previous level measurement, a new level value is added to the list every 125 milliseconds and an old value is deleted. A percentile value calculation (L AF,90% and L AF,10% ) takes place in the list.
  • the difference between these continuously determined percentile values is used to calculate level differences that can be associated with the decrease in performance loss, as the previously described study by Renz et al. (2016) shows.
  • the higher the relative level of the activity transients L AF,10% the greater the distraction (Zuydervliet et al., 2008).
  • the background sound level L AF,90% should be increased by adding masking noise to such an extent that the level difference to the activity sound level L AF,10% is sufficiently reduced.
  • the control described below ensures that the difference between these two values is as small as possible (e.g. below 3 or e.g. between 2 and 3. Other target values can also be selected).
  • the total signal from masking sound and background sound is examined for its L AF,10%-90% value.
  • Fig.3 illustrates the part of the algorithm, the functionality of which is described below.
  • the masking sound is played through headphones, which means that an analysis of the actual percentile values should be carried out at the position of the user's ear.
  • an exact analysis of this sound condition would only be possible using a microphone located in the headphone capsule.
  • ANC headphones usually have such a microphone built into the capsules, but the microphone signal cannot be used without knowledge of the integrated signal processing of the respective headphones, if it is even possible to pick up this signal.
  • the algorithm should also be universally usable with headphones without ANC. Therefore, the signal that reaches the user's ear is estimated in such embodiments. If access to the microphone is possible, the value can also be determined directly. The subsequent control is then carried out with the measured value instead of the estimated one, but is otherwise identical.
  • the background noise is reduced in level by the headphones used.
  • the determined equivalent continuous sound levels of the background noise condition are offset against the attenuation factor in this part of the algorithm in order to obtain an estimated relative sound level of the background noise condition at the position of the user's ear.
  • the masking sound signal (and the harmonic component) which was tapped after its level adjustment (see Fig.4 ), a level measurement is now also carried out.
  • the values determined are added to the background noise multiplied by the damping factor, whereby the estimated relative total noise level L AF,10%-90%, total can be determined.
  • a person who uses an implementation of the adaptive masking signal generator algorithm in hardware or software can adjust the masking sound generator's playback volume for the current sound condition at the beginning of use. This is done, for example, via a fader in the graphical user interface, or a potentiometer on the headphones, which controls the calibration factor W.
  • the calibration factor is added independently of the level control at the end of the signal path.
  • the algorithm analyses the input signal of the microphone and calculates level differences of L AF,10% and L AF,90% (see Fig.3 These level differences L AF,10%-90%,HSB are intended to regulate the masking sound in its volume per octave band.
  • the relationship of the prediction parameter L AF,10%-90% is not linear to a prospective DP value (Renz, 2019). This means that simply increasing the volume of the masking sound by the determined L AF,10%-90%,HSB value does not necessarily sufficiently mask the disturbing sound components of the HSB.
  • the signal that reaches the user's ear attenuated ambient sound + masking signal
  • it is analyzed as described above to determine the total value of the percentile differences L AF,10%-90%,Ges .
  • L AF,10%-90%,Ges should be compared with a target value L AF,10%-90%,Ziel .
  • a suitable target value at which a drop in performance does not yet occur significantly is between 2 dB and 3 dB.
  • a target value L AF,10%-90%,Ziel of 2.5 dB is used for the algorithm. This leads to a target value range between 2 dB and 3 dB, within which L AF,10%-90%,Ges moves.
  • the target value can also be chosen differently.
  • the error value e describes the difference between L AF,10%-90%,Ziel and L AF,10%-90%,Ges .
  • the manipulated variable u which regulates the volume of the masking sound, is defined as the sum of L AF,10%-90%,HSB and a correction value z (see equation 1).
  • u L AF , 10 % ⁇ 90 % , HSB + z
  • the correction value z must increase until an error value of 0 is reached. As soon as the error value falls below 0, the correction value must continuously decrease again. The correction value will increase and decrease again until it reaches a value at which the error value remains constant at 0. However, the correction value z should increase or decrease more slowly the closer the error value approaches 0. Since there is a tolerance range of +/- 0.5 dB around L AF,10%-90%,target , z can increase or decrease constantly until the tolerance limit is reached. From an error value of 0.5, z should change in smaller steps the closer e approaches 0. This is to prevent the error value from being corrected beyond the zero point by too strong a correction.
  • the current correction value zn is the result of the last correction value zn -1, added or subtracted with a correction flat rate g(e).
  • This correction flat rate depends on the size of the error value e, and is clearly defined for different conditions (see equation 3). This part of the algorithm is described in Fig.5 shown.
  • Masking sounds should generally have a maximum sound pressure level between 45 dB(A) and 48 dB(A). This maximum is justified by the fact that higher sound levels over a longer period of time are usually perceived as extremely disturbing (Haapakangas et al., 2011). Therefore, the algorithm limits the upper and lower limits of u. However, the masking system described in this invention report cannot detect absolute sound levels, which is why the maximum possible sound level values are controlled via the user's own calibration.
  • the dynamic range of the adaptive masking signal is set to 26 dB, but can be changed depending on the implementation. This means that even with a slightly disturbing HSB, the L AF,10%-90%,target value can be achieved, whereby the masking sound is as quiet as possible.
  • Equation 4 describes the current level value p n , which defines the last output level value p n -1 and t Attack and t Release , through the current input value u n (control value).
  • the time parameters tAttack and t Release result from the input sample rate and the desired attack and release times.
  • the determined L AF,90% values are continuously checked for strong fluctuations. If a newly arrived L AF,90% value falls by more than 2 dB compared to the last L AF,90% value, the attack time t Attack of the time ramp is set to 90 seconds for the duration of the observation period (5 seconds). An attack time of this magnitude means that no noticeable increase in level is possible. After the five seconds have elapsed, the attack time is reset to its regular value and the level can be regulated regularly again.
  • the entire adaptive harmonic speech masking signal (consisting of the masking and harmonic components) is reproduced via the audio output of the terminal device (digital or analog) through headphones.
  • a masking signal which is both pleasant and effective and reliably achieves psychoacoustically determined target values within a specified time interval, the correlation of which with cognitive performance is known, for example.
  • Embodiments are based on the fact that the control adjusts the masker by means of an estimation depending on the expected interference effect.
  • embodiments can be used in office spaces, especially in offices for several people, and can be adapted in particular for use with headphones.
  • Other areas of application can be, for example, in medical use or in therapy, or even in tourism.
  • aspects have been described in the context of a device, it is to be understood that these aspects also represent a description of the corresponding method, so that a block or component of a device can also be understood as a corresponding method step or as a feature of a method step. Analogously, aspects described in the context of or as a method step also represent a description of a corresponding block or detail or feature of a corresponding device.
  • Some or all of the method steps can be performed by a hardware apparatus (or using a hardware apparatus), such as a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, some or more of the key method steps can be performed by such an apparatus.
  • embodiments of the invention may be implemented in hardware or in software, or at least partially in hardware or at least partially in software.
  • the implementation may be carried out using a digital storage medium, for example a floppy disk, a DVD, a BluRay disc, a CD, a ROM, a PROM, an EPROM, an EEPROM or a FLASH memory, a hard disk or another magnetic or optical memory on which electronically readable control signals are stored that can interact or interact with a programmable computer system in such a way that the respective method is carried out. Therefore, the digital storage medium may be computer readable.
  • Some embodiments according to the invention thus comprise a data carrier having electronically readable control signals capable of interacting with a programmable computer system such that one of the methods described herein is carried out.
  • embodiments of the present invention may be implemented as a computer program product having a program code, wherein the program code is operable to perform one of the methods when the computer program product is run on a computer.
  • the program code can, for example, also be stored on a machine-readable medium.
  • an embodiment of the method according to the invention is thus a computer program that has a program code for carrying out one of the methods described herein when the computer program runs on a computer.
  • a further embodiment of the methods according to the invention is thus a data carrier (or a digital storage medium or a computer-readable medium) on which the computer program for carrying out one of the methods described herein is recorded.
  • the data carrier or the digital storage medium or the computer-readable medium is typically tangible and/or non-transitory.
  • a further embodiment of the method according to the invention is thus a data stream or a sequence of signals which represents the computer program for carrying out one of the methods described herein.
  • the data stream or the sequence of signals can be configured, for example, to be transferred via a data communication connection, for example via the Internet.
  • a further embodiment comprises a processing device, for example a computer or a programmable logic device, which is configured or adapted to carry out one of the methods described herein.
  • a processing device for example a computer or a programmable logic device, which is configured or adapted to carry out one of the methods described herein.
  • a further embodiment comprises a computer on which the computer program for carrying out one of the methods described herein is installed.
  • a further embodiment according to the invention comprises a device or a system which is designed to transmit a computer program for carrying out at least one of the methods described herein to a receiver.
  • Transmission may be, for example, electronic or optical.
  • the recipient may be, for example, a computer, a mobile device, a storage device or a similar device.
  • the device or system may, for example, comprise a file server for transmitting the computer program to the recipient.
  • a programmable logic device e.g., a field programmable gate array, an FPGA
  • a field programmable gate array may interact with a microprocessor to perform any of the methods described herein.
  • the methods are performed by any hardware device. This may be general-purpose hardware such as a computer processor (CPU) or hardware specific to the method such as an ASIC.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)
EP22205855.4A 2022-11-07 2022-11-07 Appareil et procédé de génération adaptative de sons de masquage vocaux harmoniques Pending EP4365890A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP22205855.4A EP4365890A1 (fr) 2022-11-07 2022-11-07 Appareil et procédé de génération adaptative de sons de masquage vocaux harmoniques
PCT/EP2023/080738 WO2024099913A1 (fr) 2022-11-07 2023-11-03 Dispositif et procédé de génération de bruit de masquage vocal harmonique adaptatif

Applications Claiming Priority (1)

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EP22205855.4A EP4365890A1 (fr) 2022-11-07 2022-11-07 Appareil et procédé de génération adaptative de sons de masquage vocaux harmoniques

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EP4365890A1 true EP4365890A1 (fr) 2024-05-08

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