# US 12,763,086 B2 · High privacy DSP-based audio anonymization with audio segmentation and randomization

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- Number: US 12,763,086 B2
- Document ID: US12763086B2
- Status: Granted
- Kind: B2
- Application number: 18/747,173
- Filed: 2024-06-18
- Published: 2025-12-18
- Granted: 2026-10-13
- Earliest claimed: 2024-06-18
- Assignees: Huawei Technologies Co., Ltd.
- Applicants: Huawei Technologies Co., Ltd.
- Inventors: Ting Pan; Josip Tony Smolcic; Zao Yang; Tongyu Ge; Jinxin Liu
- Web page: https://patentgrep.com/patent/US12763086B2

## CPC

- G10L 21/013: Adapting to target pitch
- G10L 17/02: Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
- G10L 17/26: Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
- G10L 25/90: Pitch determination of speech signals
- G10L 2021/0135: {Voice conversion or morphing}
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

A method and an electronic device for generating an anonymized audio output are provided. The method, executable by the electronic device, comprises acquiring an audio recording of a speaker; stochastically determining a base pitch value based on at least a first probabilistic function; segmenting the original audio input into a plurality of audio segments, each of the plurality of audio segments being associated with a respective pitch. For each audio segment, the method further comprises generating a pitch adjustment value using a combination of the base pitch value of the segment and a value determined using a second probabilistic function; generating an adjusted audio segment by adjusting the pitch of the audio segment using the pitch adjustment value, the adjusted audio segment having an adjusted pitch that is different from the original pitch; generating the anonymized audio output by combining the adjusted audio segments.

## Claims

### Claim 1

A method of generating an anonymized audio output, the method executable by a processor, the method comprising:
acquiring an original audio input, the original audio input being an audio recording of a speaker;
stochastically determining a base pitch value based on at least a first probabilistic function;
segmenting the original audio input into a plurality of audio segments, each of the plurality of audio segments being associated with a respective pitch;
for a first audio segment from the plurality of audio segments:
generating a first pitch adjustment value using a combination of a first value and the base pitch value, the first value being determined using a second probabilistic function, wherein the first value comprises a random shift value that shifts the base pitch value up or down within a fixed pitch range for the first audio segment, the fixed pitch range being selected such that pitch-shifting distortion does not impact interpretability of the original audio;
generating a first adjusted audio segment by adjusting a first pitch of the first audio segment using the first pitch adjustment value, the first adjusted audio segment having a first adjusted pitch that is different from the first pitch;
generating the anonymized audio output using the first adjusted audio segment.

### Claim 2 (depends on 1)

The method of claim 1, wherein the method further comprises:
determining a gender of the speaker using the original audio input; and
wherein the stochastically determining a base pitch value is further based on the gender of the speaker.

### Claim 3 (depends on 1)

The method of claim 1, wherein the method further comprises:
for a second audio segment from the plurality of audio segments:
generating a second pitch adjustment value using a combination of a second value and the base pitch value,
the second value being different from the first value, the second pitch adjustment value being different from the first pitch adjustment value;
generating a second adjusted audio segment by adjusting a second pitch of the second audio segment using the second pitch adjustment value, the second adjusted audio segment having a second adjusted pitch that is different from the second pitch, wherein the second value comprises a second random shift value that shifts the base pitch value up or down within the fixed pitch range for the second audio segment;
and wherein the generating the anonymized audio output further comprises using the second adjusted audio segment.

### Claim 4 (depends on 2)

The method of claim 2, wherein the method further comprises extracting a plurality of features from the original audio input using a feature extraction model, and wherein the determining the gender further comprises:
inputting the plurality of features into a gender classification model; and
outputting a gender class value by the gender classification model, the gender class value being indicative of the gender of the speaker in the original audio input.

### Claim 5 (depends on 4)

The method of claim 4, wherein the feature extraction model is at least one of: a Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN), Gaussian Mixture Models (GMM).

### Claim 6 (depends on 1)

The method of claim 1, wherein the segmenting the original audio input comprises:
employing a segmentation model to segment the original audio input into the plurality of audio segment, the segmentation model being at least one of: Hidden Markov Models (HMMs), and Gaussian Mixture Models (GMM).

### Claim 7 (depends on 4)

The method of claim 4, wherein the method further comprises:
generating the first value using the gender classification model based on an extracted pitch.

### Claim 8 (depends on 1)

The method of claim 1, wherein the method further comprises:
generating another first adjusted audio segment using a time-scale modification based pitch shifting algorithm and the first adjusted audio segment; and
generating the anonymized audio output using the another first adjusted audio segment.

### Claim 9 (depends on 8)

The method of claim 8, wherein the time-scale modification based pitch shifting algorithm is at least one of: Phase Vocoder (PV), Synchronous Overlap and Add (SOLA), Pitch-Synchronous Overlap and Add (PSOLA), and Waveform Similarity Overlap-Add (WSOLA).

### Claim 10 (depends on 1)

The method of claim 1, wherein the method further comprises:
triggering transmission, to a server over a communication network, of the anonymized audio output in lieu of the original audio input.

### Claim 11

An electronic device comprising a non-transitory computer-readable medium and a processor for generating an anonymized audio output, the non-transitory computer-readable medium comprising instructions, which upon being executed by the processor, configure the processor to:
acquire an original audio input, the original audio input being an audio recording of a speaker;
stochastically determine a base pitch value based on at least a first probabilistic function;
segment the original audio input into a plurality of audio segments, each of the plurality of audio segments being associated with a respective pitch;
for a first audio segment from the plurality of audio segments:
generate a first pitch adjustment value using a combination of a first value and the base pitch value, the first value being determined using a second probabilistic function, wherein the first value comprises a random shift value that shifts the base pitch value up or down within a fixed pitch range for the first audio segment, the fixed pitch range being selected such that pitch-shifting distortion does not impact interpretability of the original audio;
generate a first adjusted audio segment by adjusting a first pitch of the first audio segment using the first pitch adjustment value, the first adjusted audio segment having a first adjusted pitch that is different from the first pitch;
generate the anonymized audio output using the first adjusted audio segment.

### Claim 12 (depends on 11)

The electronic device of claim 11, wherein the processor is further configured to:
determining a gender of the speaker using the original audio input; and
wherein the stochastically determining a base pitch value is further based on the gender of the speaker.

### Claim 13 (depends on 11)

The electronic device of claim 11, wherein the processor is further configured to:
for a second audio segment from the plurality of audio segments:
generate a second pitch adjustment value using a combination of a second value and the base pitch value,
the second value being different from the first value, the second pitch adjustment value being different from the first pitch adjustment value, wherein the second value comprises a second random shift value that shifts the base pitch value up or down within the fixed pitch range for the second audio segment;
generate a second adjusted audio segment by adjusting a second pitch of the second audio segment using the second pitch adjustment value, the second adjusted audio segment having a second adjusted pitch that is different from the second pitch;
and wherein the generating the anonymized audio output further comprises using the second adjusted audio segment.

### Claim 14 (depends on 12)

The electronic device of claim 12, wherein the processor is further configured to extract a plurality of features from the original audio input using a feature extraction model, and wherein the determining the gender further comprises:
inputting the plurality of features into a gender classification model;
outputting a gender class value by the gender classification model, the gender class value being indicative of the gender of the speaker in the original audio input.

### Claim 15 (depends on 14)

The electronic device of claim 14, wherein the feature extraction model is at least one of: a Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN), Gaussian Mixture Models (GMM).

### Claim 16 (depends on 11)

The electronic device of claim 11, wherein the segmenting the original audio input comprises:
employing a segmentation model to segment the original audio input into the plurality of audio segment, the segmentation model being at least one of: Hidden Markov Models (HMMs), and Gaussian Mixture Models (GMM).

### Claim 17 (depends on 14)

The electronic device of claim 14, wherein the processor is further configured to:
generate the first value using the gender classification model based on an extracted pitch.

### Claim 18 (depends on 11)

The electronic device of claim 11, wherein the processor is further configured to:
generate another first adjusted audio segment using a time-scale modification based pitch shifting algorithm and the first adjusted audio segment; and
generate the anonymized audio output using the another first adjusted audio segment.

### Claim 19 (depends on 18)

The electronic device of claim 18, wherein the time-scale modification based pitch shifting algorithm is at least one of: Phase Vocoder (PV), Synchronous Overlap and Add (SOLA), Pitch-Synchronous Overlap and Add (PSOLA), and Waveform Similarity Overlap-Add (WSOLA).

### Claim 20 (depends on 11)

The electronic device of claim 11, wherein the processor is further configured to:
trigger transmission, to a server over a communication network, of the anonymized audio output in lieu of the original audio input.