Wearable electronic device for emitting a masking signal
A signal processing method and a wearable electronic device such as a headphone or an earphone comprising a microphone arranged to pick up an acoustic signal and convert the acoustic signal to a microphone signal (x); a loudspeaker arranged in an earpiece; and a processor configured to control the volume of a masking signal (m); and supply the masking signal (m) to the loudspeaker. Further, the processor is further configured to detect voice activity and generate a voice activity signal (y) which is, concurrently with the microphone signal, sequentially indicative of one or more of: voice activity and voice in-activity; and control the volume of the masking signal (m) in response to the voice activity signal (y) in accordance with supplying the masking signal (m) to the loudspeaker at a first volume at times when the voice activity signal (y) is indicative of voice activity and at a second volume at times when the voice activity signal (y) is indicative of voice in-activity.
1. A wearable electronic device comprising:
an electro-acoustic input transducer configured to:
pick up an acoustic signal, and
convert the acoustic signal to a microphone signals (x);
a loudspeaker; and
a processor comprising:
a voice activity detector configured to detect voice activity based on processing time-domain waveforms of the microphone signals (x) by:
generating frames comprising frequency-time representations of the waveforms of the microphone signal (x), and
detecting the voice activity only when voice activity is determined to be in a predefined number of consecutive frames;
a machine learning component configured to:
generate a voice activity signal (y), concurrently with the microphone signal (x), by indicating periods of time in which the microphone signal (x) includes signal components that represent detected voice activity and signal components that represent detected voice inactivity; and
wherein the generated voice activity signal (y) is sequentially indicative of at least one voice activity and voice inactivity;
control a volume of a masking signal (m) in response to the generated voice activity signal (y) to be at a first volume when the generated voice activity signal (y) is indicative of voice activity and to be at a second volume when the generated voice activity signal (y) is indicative of voice inactivity; and
supply the masking signal (m) to the loudspeaker.
2. A wearable device according to claim 1 , wherein the processor is configured with at least one of:
an audio player to generate the masking signal by playing an audio track; and
an audio synthesizer to generate the masking signal using one or more signal generators.
3. A wearable device according to claim 1 , wherein the generated frames comprise values arranged in frequency bins.
4. A wearable device according to claim 1 , wherein the processor controls the masking signal (m) in accordance with a time and frequency distribution of the envelope of the masking signal substantially matching the voice activity signal or the envelope of the voice activity signal, which is in accordance with the frequency-time representation.
5. A wearable device according to claim 1 , wherein the processor is further configured to:
gradually increase the volume of the masking signal (m) over time in response to detecting an increasing frequency or density of voice activity.
6. A wearable device according to claim 1 , wherein the processor further comprises:
a mixer to generate the masking signal (m) from one or more selected intermediate masking signals from multiple intermediate masking signals, and
wherein selection of the one or more selected intermediate masking signals is performed in accordance with a criterion based on one or both of the microphone signal and the voice activity signal.
7. A wearable device according to claim 1 , wherein the processor further comprises:
a gain stage, configured with a trigger for attack amplitude modulation of an intermediate masking signal and a trigger for decay amplitude modulation of the intermediate masking signal;
wherein the gain stage is triggered to perform attack amplitude modulation of the intermediate masking track in response to detecting a transition from voice in-activity to voice activity and to perform decay amplitude modulation of the intermediate masking track in response to detecting a transition from voice activity to voice in-activity.
8. A wearable device according to claim 1 , wherein the processor further comprises:
an active noise cancellation unit to process the microphone signal (x) and supply an active noise cancellation signal (q) to the loudspeaker; and
a mixer to mix the active noise cancellation signal (q) and the masking signal (m) into a signal for the loudspeaker.
9. A wearable device according to claim 1 ,
wherein the processor is further configured to selectively operate in a first mode or a second mode;
wherein, in the first mode, the processor:
controls the volume of the masking signal (m) supplied to the loudspeaker; and
wherein, in the second mode, the processor:
forgoes supplying the masking signal (m) to the loudspeaker at the first volume irrespective of the voice activity signal (y) being indicative of voice activity.
10. A wearable device according to claim 1 ,
wherein the electro-acoustic input transducer is a first microphone outputting a first microphone signal (x); and
wherein the wearable device comprises:
a second microphone outputting a second microphone signal (x′); and
a beam-former coupled to receive the first microphone signal (x) or a third microphone signal from a third microphone and the second microphone signal (x′) and to generate a beam-formed signal.
11. A signal processing method at a wearable electronic device comprising:
an electro-acoustic input transducer arranged to pick up an acoustic signal and convert the acoustic signal to a microphone signal (x); a loudspeaker; and
a processor comprising a machine learning component that is configured to:
control a volume of a masking signal (m);
supply a masking signal (m) to the loudspeaker;
detect voice activity based on processing at least of time-domain waveforms of the microphone signal (x) that includes:
generating frames comprising frequency-time representations of the waveforms of the microphone signal (x), and
determining the voice activity only when voice activity is determined to be in a predefined number of consecutive frames; and
generate a voice activity signal (y) which is, concurrently with the microphone signal, sequentially indicative of one or more of voice activity and voice in-activity; and
control the volume of the masking signal (m) in accordance with a sound pressure level of the acoustic signal when supplying the masking signal (m) to the loudspeaker at a first volume at times when the voice activity signal (y) is indicative of voice activity and at a second volume at times when the voice activity signal (y) is indicative of voice in-activity.
12. The wearable electronic device according to claim 1 , wherein the machine learning component is a recurrent neural network.
13. The wearable electronic device according to claim 1 , wherein the machine learning component is a convolutional neural network.
14. The signal processing method according to claim 11 , wherein the machine learning component is a recurrent neural network.
15. The signal processing method according to claim 11 , wherein the machine learning component is a convolutional neural network.