# US 12,763,083 B2 · Bandwidth extension of incoming data using neural networks > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,763,083 B2 - Document ID: US12763083B2 - Status: Granted - Kind: B2 - Application number: 18/780,377 - Filed: 2024-07-22 - Published: 2025-01-16 - Granted: 2026-10-13 - Earliest claimed: 2019-04-30 - Assignees: GDM Holding LLC - Applicants: GDM Holding LLC - Inventors: Ioannis Alexandros Assael; Thomas Chadwick Walters; Archit Gupta; Brendan Shillingford - Web page: https://patentgrep.com/patent/US12763083B2 ## CPC - G10L 19/0204: {using subband decomposition} - G06N 3/08: Learning methods - G10L 25/18: the extracted parameters being spectral information of each sub-band - G10L 25/30: using neural networks - G10L 25/57: for processing of video signals - H04N 7/0125: {one of the standards being a high definition standard} - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bandwidth extension. One of the methods includes obtaining a low-resolution version of an input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period. Generating the high-resolution version includes generating a representation of the low-resolution version of the input; processing the representation of the low-resolution version of the input through a conditioning neural network to generate a conditioning input; and processing the conditioning input using a generative neural network to generate the high-resolution version of the input. ## Claims ### Claim 1 A method performed by one or more computers and for improving communication quality on a data communications network, the method comprising: receiving, at a communications node of the data communications network, an encoded input signal representing an input; decoding, at the communications node of the data communications network, the encoded input signal using a decoder of a codec to generate a low-resolution version of the input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input generated by decoding the encoded input signal, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period, the generating comprising: generating, from the low-resolution version of the input generated by decoding the input signal, a conditioning input; and processing the conditioning input generated from the low-resolution version of the input using a generative neural network to generate the high-resolution version of the input. ### Claim 2 (depends on 1) The method of claim 1, wherein the input is audio data, wherein the low-resolution version of the input comprises an audio waveform at the first sample rate, and wherein the high-resolution version of the input comprises an audio waveform at the second sample rate. ### Claim 3 (depends on 2) The method of claim 2, wherein the audio data is an utterance, the low-resolution version is a low-resolution speech signal of the utterance, and the high-resolution version is a high-resolution speech signal of the utterance. ### Claim 4 (depends on 1) The method of claim 1, wherein generating, from the low-resolution version of the input generated by decoding the input signal, a conditioning input comprises: generating a representation of the low-resolution version of the input; and generating the conditioning input from the representation of the low-resolution version of the input. ### Claim 5 (depends on 4) The method of claim 4, wherein the input is audio data, and wherein generating the representation of the low-resolution version of the input comprises: generating, from a raw waveform of the low-resolution version, a spectrogram of the low-resolution version. ### Claim 6 (depends on 4) The method of claim 4, wherein the spectrogram is a log-mel spectrogram. ### Claim 7 (depends on 4) The method of claim 4, wherein the input is audio data, and wherein generating the representation of the low-resolution version of the input comprises: using a raw waveform of the low-resolution version of the input as the representation. ### Claim 8 (depends on 4) The method of claim 4, wherein generating the conditioning input from the representation of the low-resolution version of the input comprises: processing the representation of the low-resolution version of the input using a conditioning neural network to generate the conditioning input. ### Claim 9 (depends on 1) The method of claim 1 wherein the conditioning neural network comprises a stack of convolutional layers. ### Claim 10 (depends on 9) The method of claim 9, wherein the conditioning neural network comprises one or more transpose convolutional layers following the stack of convolutional layers. ### Claim 11 (depends on 1) The method of claim 1, wherein the generative neural network is an auto-regressive neural network that generates each sample in the high-resolution version of the input conditioned on (i) the conditioning input and (ii) any samples in the high-resolution version of the input that have already been generated. ### Claim 12 (depends on 11) The method of claim 11, wherein the generative neural network is a convolutional neural network comprising dilated convolutional layers. ### Claim 13 (depends on 12) The method of claim 12, wherein an activation function of at least some of the dilated convolutional layers is conditioned on the conditioning input. ### Claim 14 (depends on 11) The method of claim 11, wherein the generative neural network is a recurrent neural network. ### Claim 15 (depends on 1) The method of claim 1, wherein the generative neural network is a feedforward neural network that is conditioned on (i) the conditioning input and (ii) a noise vector. ### Claim 16 (depends on 1) The method of claim 1, wherein the input is a video, and wherein the low-resolution version of the input comprises video frames at a first frame rate, and wherein the high-resolution version of the input comprises video frames at a second, higher frame rate. ### Claim 17 A system comprising one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to perform operations for improving communication quality on a data communications network, the operations comprising: receiving, at a communications node of the data communications network, an encoded input signal representing an input; decoding, at the communications node of the data communications network, the encoded input signal using a decoder of a codec to generate a low-resolution version of the input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input generated by decoding the encoded input signal, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period, the generating comprising: generating, from the low-resolution version of the input generated by decoding the input signal, a conditioning input; and processing the conditioning input generated from the low-resolution version of the input using a generative neural network to generate the high-resolution version of the input. ### Claim 18 One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for improving communication quality on a data communications network, the operations comprising: receiving, at a communications node of the data communications network, an encoded input signal representing an input; decoding, at the communications node of the data communications network, the encoded input signal using a decoder of a codec to generate a low-resolution version of the input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input generated by decoding the encoded input signal, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period, the generating comprising: generating, from the low-resolution version of the input generated by decoding the input signal, a conditioning input; and processing the conditioning input generated from the low-resolution version of the input using a generative neural network to generate the high-resolution version of the input. ### Claim 19 (depends on 17) The system of claim 17, wherein the input is audio data, wherein the low-resolution version of the input comprises an audio waveform at the first sample rate, and wherein the high-resolution version of the input comprises an audio waveform at the second sample rate. ### Claim 20 (depends on 19) The system of claim 19, wherein the audio data is an utterance, the low-resolution version is a low-resolution speech signal of the utterance, and the high-resolution version is a high-resolution speech signal of the utterance.