Patent Description:
Digital representations of human voices carry a large amount of information about the owner of the voice, including indicators of potential health conditions that the individual may not wish to disclose to third parties. Yet these representations and the representation process are not directly controlled by the individual whose voice is digitized. Moreover, audio representations are increasingly being used by predictive models to predict the early onset of, or to identify the presence of, an illness across a wide variety of conditions, including central nervous system (CNS) disorders, depression, autism spectrum disorder, viral infection, and even heart disease. An individual is generally unaware when the individual's voice is being analyzed for health diagnostic purposes and, even when the individual is aware, the features are often inexplicably linked to the audio of the individual's voice and cannot be easily omitted. <NPL>, describes a novel deep neural network architecture to effectively localize potential biomarkers in medical images, when only the image-level labels are available during model training. The proposed architecture combines a CNN classifier and a generative adversarial network (GAN) in a novel way, such that the CNN classifier and the discriminator in the GAN can effectively help the encoder-decoder in the GAN to remove biomarkers. <NPL>" describes a novel semi-supervised multimodal GAN framework to detect engagement levels in video conversations based on psychology literature. There are three constructs: behavioral, cognitive, and affective engagement, which are used to extract various features that can effectively capture engagement levels. The features to the semi-supervised GAN network that does regression using these latent representations to obtain the corresponding valence and arousal values, which are then categorized into different levels of engagements.

Principles of the invention provide techniques for obfuscating audio samples for health privacy contexts. According to an aspect of the present invention, there is provided a method according to claim <NUM>.

According to another aspect of the present invention, there is provided an apparatus according to claim <NUM>.

According to another aspect of the present invention, there is provided a computer program product for federated learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising training a supervised discriminator to detect bio-markers in an audio sample dataset; training a denoising autoencoder to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample of the audio sample dataset; training a conditional auxiliary generative adversarial network (GAN) to generate the output audio sample with the same fidelity as the input audio sample, wherein the output audio sample is void of the bio-markers; and deploying the conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder in an audio processing system.

As used herein, "facilitating" an action includes performing the action, making the action easier, helping to carry the action out, or causing the action to be performed. Thus, by way of example and not limitation, instructions executing on one processor might facilitate an action carried out by instructions executing on a remote processor, by sending appropriate data or commands to cause or aid the action to be performed. For the avoidance of doubt, where an actor facilitates an action by other than performing the action, the action is nevertheless performed by some entity or combination of entities.

One or more embodiments of the invention or elements thereof can be implemented in the form of a computer program product including a computer readable storage medium with computer usable program code for performing the method steps indicated. Furthermore, one or more embodiments of the invention or elements thereof can be implemented in the form of a system (or apparatus) including a memory, and at least one processor that is coupled to the memory and operative to perform exemplary method steps. Yet further, in another aspect, one or more embodiments of the invention or elements thereof can be implemented in the form of means for carrying out one or more of the method steps described herein; the means can include (i) hardware module(s), (ii) software module(s) stored in a computer readable storage medium (or multiple such media) and implemented on a hardware processor, or (iii) a combination of (i) and (ii); any of (i)-(iii) implement the specific techniques set forth herein.

Techniques of the present invention can provide substantial beneficial technical effects. For example, one or more embodiments provide one or more of: an audio codec with embedded bio-marker obfuscation capabilities; an obfuscation method that effectively obfuscates an audio sample, thus hiding features that may be used by a predictive model to infer health conditions; obfuscation techniques suitable for use in smartwatches, smartphones, home devices and the like; technological improvements in privacy and security for computerized audio processing systems utilized in call centers, interactive voice response (IVR) systems, speech recognition applications, and the like, by masking voice traits indicative of private characteristics of an individual, to prevent invasion of privacy, while allowing the speech/audio to remain understandable; and obfuscation methods that are bio-marker agnostic and maintain the quality and fidelity of the audio sample while not impacting the compression compute time.

Some embodiments may not have these potential advantages and these potential advantages are not necessarily required of all embodiments. These and other features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.

Workloads layer <NUM> provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation <NUM>; software development and lifecycle management <NUM>; virtual classroom education delivery <NUM>; data analytics processing <NUM>; transaction processing <NUM>; and an audio processing component <NUM> that implements health privacy obfuscating techniques for audio samples.

Generally, systems and methods for processing audio are disclosed. Digital representations of human voices carry a large amount of information about the owner of the voice, including indicators of potential health conditions that the individual may not wish to disclose to third parties. Yet these representations and the representation process are not directly controlled by the individual whose voice is digitized. Moreover, the representations are increasingly being used by predictive models to predict the early onset of, or to identify the presence of, an illness across a wide variety of conditions, including central nervous system (CNS) disorders, depression, autism spectrum disorder, viral infection and even heart disease. An individual is generally unaware when the individual's voice is being analyzed for health diagnostic purposes and, even when the individual is aware, the features are often inexplicably linked to the audio of the individual's voice and cannot be easily omitted.

In one example embodiment, an audio sample is effectively obfuscated in regard to health conditions and their associated bio-markers, thus hiding features that may be used by a predictive model to infer health conditions, while still maintaining the quality and fidelity of the audio sample. One or more embodiments are, advantageously, not autoregressive; thus, inference is fast and can be used in an audio codec library (which converts audio to a bitstream). In one example embodiment, no explicit input or configuration is needed from the actor (the individual whose voice is analyzed). Thus, one or more embodiments are suitable for use in smartwatches, smartphones, home devices, and the like. In one or more embodiments, the bio-marker is agnostic in the sense that labelled training samples are not required. The disclosed audio processor may be implemented as part of a CODEC library on a machine (e.g. FLAC (Free Lossless Audio Codec), an audio coding format for lossless compression of digital audio), as a plugin into an application that requires audio input or a plugin to a browser, as a recording application that stores audio samples for use in authentication contexts, and the like.

Dysphonia, including jitter and shimmer features in voice, is often used to diagnose Parkinson's disease, increasingly by automated systems. While other illness may be less conspicuous, these analysis techniques may still be used for activities that have serious implications for the actor. For example, an actor suffering from Parkinson's may apply for a loan from a bank where the actor has to converse on the phone with a loan officer. If the bank has a diagnostic model running in the background that monitors the actor's voice and detects the actor's health condition, the bank may choose to deny the loan for a medical reason. While the actor may be unable to control the bank's use of the actor's voice, the actor can obfuscate the relevant features of the actor's voice while maintaining the fidelity of other aspects of the actor's voice.

In one example embodiment, an exemplary obfuscation system obfuscates one or more types of bio-markers. Audio format is being used by predictive models to predict the presence of an illness across a wide variety of conditions, including CNS disorders, depression, autism spectrum disorder, covid, heart disease, and the like. For example, any bio-markers that may be detected in the frequency domain by training a deep learning model may be utilized (mel-frequency cepstral coefficients (MFCC) representations and its variants are a non-limiting example). In one example embodiment, the predictive models do not require labelled examples of audio samples containing the bio-markers for training. In other words, one or more embodiments of the system are bio-marker-agonistic. One or more embodiments can also be extended to other contexts, e.g., preventing implicit discrimination based on accent, workplace cultural diagnostics, and the like.

<FIG> is a block diagram of an example audio obfuscation environment <NUM>, in accordance with an example embodiment. In one example embodiment, user audio <NUM> is obtained by a device <NUM>. The device <NUM> may be a smartphone <NUM>, a smartwatch <NUM>, a home device <NUM>, <NUM>, a personal computer, and the like. The device <NUM> may receive a digital audio stream or digital audio file, may convert the speech of a user to a digital audio stream or digital audio file, and the like. In one example embodiment, the device <NUM> executes an audio obfuscation method, as described more fully below in conjunction with <FIG>. In one example embodiment, the device <NUM> provides the user audio, in digital or analog form, to a cloud environment that implements the audio obfuscation method and returns obfuscated audio data. The audio obfuscation method utilizes a discriminator <NUM> and an autoencoder <NUM>, as described more fully below in conjunction with <FIG>. A database <NUM> that resides either internal or external to the device <NUM> maintains audio machine learning models that have been developed for corresponding bio-markers.

<FIG> is a block diagram of a portion of an example audio obfuscation system <NUM>, in accordance with an example embodiment. In one example embodiment, a discriminator <NUM> is trained on labelled medical data <NUM> containing audio samples indicative of at least one health condition (and having corresponding labels identifying the medical condition) while minimizing a classification generalization error. The input is the frequency domain representation of the audio sample <NUM> (mel-frequency spectrum). In one example embodiment, the discriminator <NUM> is a fully convolutional neural network that takes in mel-frequency cepstral coefficients (MFCC) representations of the audio sample and classifies the presence of a bio-marker, where, for example, a one represents a presence of the corresponding bio-marker and a zero represents an absence of the corresponding bio-marker.

In one example embodiment, a denoising autoencoder <NUM> is trained on clean samples <NUM>, that is, samples that do not contain bio-markers, to learn a latent representation that is used to reconstruct the input. The training to learn the latent representation that is used to reconstruct the input may be performed, for example, by minimizing a Kullback-Leibler (KL)-divergence-based reconstruction error loss plus a fidelity term based on frequency response, distortion, noise, and time-based errors.

<FIG> is a block diagram of an example audio obfuscation system <NUM>, in accordance with an embodiment. In the embodiment, a discriminator function D of the discriminator <NUM> is used as a discriminator in a generative adversarial network (GAN) setup <NUM> with the autoencoder <NUM> as the generator. Clean samples <NUM> are preprocessed using known techniques to generate clean preprocessed samples <NUM>, which are input to the discriminator <NUM>. The GAN <NUM> is trained such that the discriminator <NUM> attempts to maximize the entropy that clean data <NUM> passes through the discriminator <NUM> and is trained to minimize the entropy such that the denoised representation of bad data <NUM> (containing bio-markers) pass through the discriminator <NUM>; autoencoder <NUM> attempts to do the opposite. Note that element <NUM> represents the audio sample preprocessed with the necessary steps to be used by the discriminator <NUM>.

The generator G (autoencoder <NUM>) is frozen, and backpropagation is performed through the discriminator function D using the gradient from the GAN loss. The discriminator function D is frozen and propagation is performed through autoencoder <NUM> using the gradient from the GAN loss combined with a decaying constant times the reconstruction error loss of generator G. The decaying constant is a hyper-parameter adjusted by the type of audio dataset used and the models' structures. (The skilled artisan will be familiar with selection of hyperparameters via heuristics, and, given the teachings herein, will be able to select suitable values.

In one example embodiment, the autoencoder <NUM> is implemented as a fully convolutional neural network that takes in MFCC representations of the audio sample and produces a denoised version <NUM> of the MFCC input. The above steps are iterated (i.e., carried out iteratively) until convergence is reached. In one example embodiment, convergence is based on a Nash Equilibrium. Furthermore in this regard, in one or more embodiments, referring to <FIG>, discussed in greater detail below, all steps are repeated during the iterative process except training of the discriminator.

Once trained, the audio obfuscation system <NUM> is capable of obtaining, as input, an audio sample with a bio-marker and generating another audio sample that is almost equivalent to the obtained audio sample, but that "fools" the discriminator <NUM> (that was trained to detect such bio-markers) into not recognizing the bio-marker.

<FIG> is a block diagram of an example model distillation system <NUM>, in accordance with an example embodiment. In one example embodiment, if access to labelled samples for a given health condition is not available, the discriminator <NUM> is created through model distillation from a black box teacher model <NUM>. The discriminator <NUM> may be trained on extracted features from a mel-representation, such as linear predictive coding (LPC) parameters (filter parameters and residual signal). Features may be extracted from the sample that are to be explicitly conditioned on, for example, timbre, accent, and fidelity parameters. The skilled artisan will be familiar with hierarchical decoupling of features in generating images - that is, allowing a GAN that can generate images with certain styles to be trained. The exemplary technique of <FIG> presents and enables a novel application of model distillation to audio features.

Effectively, within the generator architecture of the example model distillation system <NUM>, a mapping network (layers) maps from a latent space into another intermediate latent space that parameterizes the high-level features, such as timbre, accent, fidelity, pitch, and the like. Given that direct access to a classifier that has been trained using labelled samples or access to labelled samples may not exist, the black box teacher model <NUM> utilizes procedures for model distillation. The input <NUM> includes unlabeled samples which may be collected from a medical corpus, provided by actors wishing to protect their privacy, and the like.

The teacher is an external system or the teacher model <NUM> of <FIG> that processes the inputs <NUM> and provides a notion of the presence of bio-markers (which can be binary or real-valued). (The teacher system may be accessed, for example, via an API.

The student model <NUM> distills the discriminatory ability of the teacher model <NUM> by minimizing a cost-function (e.g., a likelihood loss) based on the prediction of the teacher model <NUM> and its prediction of the same input <NUM>. In one example embodiment, the loss function <NUM> is defined based on the outputs provided by the teacher model <NUM> to be a likelihood loss (e.g., cross entropy), a variational loss (e.g., KL Divergence), and the like. The models (such as the student model <NUM> and the teacher model <NUM>) may be implemented using recurrent neural networks (RNNs), convolutional neural networks, or a combination of both. These networks are decomposed into input layers with the same dimension as the input (audio samples), hidden layers that include stacked and fully connected units, and an output layer with dimensions corresponding to the number of discriminatory classes (two classes in the case of biomarker or not-biomarker). The networks are trained by optimizing a set of parameters that is iteratively updated to maximize the ability of the network to accurately discriminate between classes for all labeled inputs. Given the teachings herein, the skilled artisan will be able to construct and train these networks.

In one example embodiment, the supervised discriminator <NUM> and the denoising autoencoder <NUM> are retrofitted as discriminator and generator of the GAN <NUM>, respectively. A joint loss of the entropy and the reconstruction error of the generator are optimized. In one example embodiment, dilated convolutions are leveraged to preserve the conditional features of the audio sample.

<FIG> is a block diagram of a conventional audio codec architecture <NUM> for a computer system. Audio <NUM> is processed via an analysis filterbank <NUM>, a quantization (entropy coding) unit <NUM>, and a linear predictive coding (LPC) filter <NUM> to generate an output audio bitstream <NUM>. The conventional audio codec architecture <NUM> may be modified to incorporate the disclosed obfuscation technique. In one example embodiment, the disclosed obfuscation technique is implemented between the analysis filterbank <NUM> and the quantization unit <NUM>. This serves to obfuscate bio-markers for all downstream audio tasks.

<FIG> is a flowchart for an example audio obfuscation method <NUM>, in accordance with an example embodiment. In one example embodiment, a model is trained for bio-marker detection (operation <NUM>). An autoencoder <NUM>, such as a mel-autoencoder, is trained (operation <NUM>). Protected bio-markers B and associated metrics M are obtained (operation <NUM>, can also include reconstruction error loss Rec_loss). For example, the identity of the type(s) of bio-markers to be obfuscated may be obtained. New audio samples (data) that obfuscate the bio-markers are generated (operation <NUM>). A check is performed to determine if DKL<hAE (operation <NUM>), where DKL is a Kullback-Leibler (KL)-divergence and hAE is an acceptable threshold of quality generated by the autoencoder <NUM> to ensure that the new audio has acceptable features to be used by the application. In a non-limiting exemplary use case, obtain input audio from a subject who has a certain characteristic (say Parkinson's disease) and use an embodiment of the invention to obtain new audio that is similar to the input audio but masks the Parkinson's markers.

The obfuscated audio samples are postprocessed and the bio-markers that were removed from the audio are reported, such as to the user who generated the original audio (operation <NUM>).

In one example embodiment, the model is trained for multiple types of bio-markers; the trained model and bio-markers can be used, for example, by an external party's application. In some cases, it may not be clear which bio-marker is being used, such as if information on external parties is not available; however, the potential bio-marker(s) that could be used by the third party can be estimated.

One or more embodiments include obfuscating one or more bio-markers of speech of a human subject using the conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder, so that the audio processing system has access to an intelligible version of the speech but does not have access to the one or more bio-markers of the human subject. Thus, for example, bias based on the bio-markers is prevented,.

For example, the audio processing system could be an interactive voice response (IVR) system. Thus, in one example embodiment, the conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder are deployed in an interactive voice response (IVR) system. The interactive voice system digitizes the voice (speech) of a user of the IVR system and the audio obfuscation system <NUM> removes one or more types of bio-markers from the digitized speech prior to further processing. For example, the audio obfuscation system <NUM> may remove one or more types of bio-markers from the digitized speech of the user of the IVR system of a bank such that the bio-markers cannot be used in the processing of a loan application.

Furthermore, in another example embodiment, the audio processing system could be a search system and the conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder are deployed in the search system. The search system digitizes the voice (speech) of a user of the search system and the audio obfuscation system <NUM> removes one or more types of bio-markers (that identify, for example, the demographics of the user) from the digitized speech prior to further processing. For example, the audio obfuscation system <NUM> may remove one or more types of bio-markers from the digitized speech of a user of the search system such that the bio-markers cannot be used in the search for residential real estate.

Given the discussion thus far, it will be appreciated that, in general terms, an exemplary method, according to an aspect of the invention, includes the operations of training, using at least one processor, a supervised discriminator <NUM>, <NUM> to detect bio-markers in an audio sample dataset <NUM>; training, using the at least one processor, a denoising autoencoder <NUM>, <NUM> to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample <NUM> of the audio sample dataset <NUM>; training, using the at least one processor, a conditional auxiliary generative adversarial network (GAN) <NUM> to generate the output audio sample with the same fidelity as the input audio sample <NUM>, wherein the output audio sample is void of the bio-markers; and deploying the conditional auxiliary generative adversarial network (GAN) <NUM>, the corresponding supervised discriminator <NUM>, <NUM>, and the corresponding denoising autoencoder <NUM>/<NUM> in an audio processing system <NUM>.

In one example embodiment, a classification generalization error is minimized during the training of the supervised discriminator <NUM>, <NUM>.

In one example embodiment, the training of the denoising autoencoder <NUM>, <NUM> to learn the latent space that is used to reconstruct the output audio sample is performed by minimizing a KL-divergence based reconstruction error loss plus a fidelity term.

In one example embodiment, the KL-divergence based reconstruction error loss plus the fidelity term is based on one or more of a frequency response, a distortion, noise, and time-based errors.

In one example embodiment, a discriminator function is used as the supervised discriminator <NUM>, <NUM> in the conditional auxiliary generative adversarial network (GAN) <NUM>, and the denoising autoencoder <NUM>, <NUM> as a generator, the conditional auxiliary generative adversarial network (GAN) <NUM> being trained such that the discriminator function attempts to maximize an entropy that clean samples pass through the discriminator <NUM>, <NUM> and minimize an entropy that a denoised representation of bad samples containing the bio-markers pass through the supervised discriminator <NUM>, <NUM>.

In one example embodiment, the generator is frozen and backpropagating is performed through the discriminator function using a gradient from the generative adversarial network loss.

In one example embodiment, the discriminator function is frozen, and propagating is performed through the generator using the gradient from the generative adversarial network loss combined with a decaying constant times a reconstruction error loss of the generator.

In one example embodiment, the training of the denoising autoencoder <NUM>, <NUM> and the training of the conditional auxiliary generative adversarial network <NUM> are iterated until convergence.

In one example embodiment, the supervised discriminator <NUM>, <NUM> comprises a convolutional neural network that inputs mel-frequency cepstral coefficients (MFCC) representations of the audio sample dataset <NUM> and classifies a presence of the bio-marker, where a first classification represents the presence of the corresponding bio-marker and a second classification represents an absence of the corresponding bio-marker.

In one example embodiment, the supervised discriminator <NUM>, <NUM> is created via model distillation from a black box teacher model <NUM>.

In one example embodiment, the training of the supervised discriminator <NUM>, <NUM> is based on extracted features from a mel-representation of the audio sample dataset <NUM>.

In one example embodiment, the denoising autoencoder <NUM>, <NUM> comprises a convolutional neural network that inputs MFCC representations of the audio sample dataset <NUM> and produces a denoised version of the MFCC representations.

In one example embodiment, one or more bio-markers of speech of a human subject are obfuscated using the conditional auxiliary generative adversarial network (GAN) <NUM>, the corresponding supervised discriminator <NUM>, <NUM>, and the corresponding denoising autoencoder <NUM>, <NUM> so that the audio processing system <NUM>, <NUM> has access to an intelligible version of the speech but does not have access to the one or more bio-markers of the human subject.

In one aspect, an apparatus comprises a memory and at least one processor, coupled to the memory, and operative to perform operations comprising training a supervised discriminator <NUM>, <NUM> to detect bio-markers in an audio sample dataset <NUM>; training a denoising autoencoder <NUM>, <NUM> to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample <NUM> of the audio sample dataset <NUM>; training a conditional auxiliary generative adversarial network (GAN) <NUM> to generate the output audio sample with the same fidelity as the input audio sample <NUM>, wherein the output audio sample is void of the bio-markers; and deploying the conditional auxiliary generative adversarial network (GAN) <NUM>, the corresponding supervised discriminator <NUM>, <NUM>, and the corresponding denoising autoencoder <NUM>, <NUM> in an audio processing system.

In one aspect, a computer program product for federated learning comprises a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising training a supervised discriminator <NUM>, <NUM> to detect bio-markers in an audio sample dataset <NUM>; training a denoising autoencoder <NUM>, <NUM> to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample <NUM> of the audio sample dataset <NUM>; training a conditional auxiliary generative adversarial network (GAN) <NUM> to generate the output audio sample with the same fidelity as the input audio sample <NUM>, wherein the output audio sample is void of the bio-markers; and deploying the conditional auxiliary generative adversarial network (GAN) <NUM>, the corresponding supervised discriminator <NUM>, <NUM>, and the corresponding denoising autoencoder <NUM>, <NUM> in an audio processing system.

One or more embodiments of the invention, or elements thereof, can be implemented in the form of an apparatus including a memory and at least one processor that is coupled to the memory and operative to perform exemplary method steps. <FIG> depicts a computer system that may be useful in implementing one or more aspects and/or elements of the invention, also representative of a cloud computing node according to an embodiment of the present invention. Referring now to <FIG>, cloud computing node <NUM> is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node <NUM> is capable of being implemented and/or performing any of the functionality set forth hereinabove.

In cloud computing node <NUM> there is a computer system/server <NUM>, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server <NUM> include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.

Computer system/server <NUM> may be described in the general context of computer system executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server <NUM> may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

As shown in <FIG>, computer system/server <NUM> in cloud computing node <NUM> is shown in the form of a general-purpose computing device. The components of computer system/server <NUM> may include, but are not limited to, one or more processors or processing units <NUM>, a system memory <NUM>, and a bus <NUM> that couples various system components including system memory <NUM> to processor <NUM>.

Bus <NUM> represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Computer system/server <NUM> typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server <NUM>, and it includes both volatile and non-volatile media, removable and non-removable media.

System memory <NUM> can include computer system readable media in the form of volatile memory, such as random access memory (RAM) <NUM> and/or cache memory <NUM>. Computer system/server <NUM> may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system <NUM> can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a "hard drive"). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus <NUM> by one or more data media interfaces. As will be further depicted and described below, memory <NUM> may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

Program/utility <NUM>, having a set (at least one) of program modules <NUM>, may be stored in memory <NUM> by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules <NUM> generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

Computer system/server <NUM> may also communicate with one or more external devices <NUM> such as a keyboard, a pointing device, a display <NUM>, etc.; one or more devices that enable a user to interact with computer system/server <NUM>; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server <NUM> to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces <NUM>. Still yet, computer system/server <NUM> can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter <NUM>. As depicted, network adapter <NUM> communicates with the other components of computer system/server <NUM> via bus <NUM>. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server <NUM>. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, and external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc..

Thus, one or more embodiments can make use of software running on a general purpose computer or workstation. With reference to <FIG>, such an implementation might employ, for example, a processor <NUM>, a memory <NUM>, and an input/output interface <NUM> to a display <NUM> and external device(s) <NUM> such as a keyboard, a pointing device, or the like. The term "processor" as used herein is intended to include any processing device, such as, for example, one that includes a CPU (central processing unit) and/or other forms of processing circuitry. Further, the term "processor" may refer to more than one individual processor. The term "memory" is intended to include memory associated with a processor or CPU, such as, for example, RAM (random access memory) <NUM>, ROM (read only memory), a fixed memory device (for example, hard drive <NUM>), a removable memory device (for example, diskette), a flash memory and the like. In addition, the phrase "input/output interface" as used herein, is intended to contemplate an interface to, for example, one or more mechanisms for inputting data to the processing unit (for example, mouse), and one or more mechanisms for providing results associated with the processing unit (for example, printer). The processor <NUM>, memory <NUM>, and input/output interface <NUM> can be interconnected, for example, via bus <NUM> as part of a data processing unit <NUM>. Suitable interconnections, for example via bus <NUM>, can also be provided to a network interface <NUM>, such as a network card, which can be provided to interface with a computer network, and to a media interface, such as a diskette or CD-ROM drive, which can be provided to interface with suitable media.

Accordingly, computer software including instructions or code for performing the methodologies of the invention, as described herein, may be stored in one or more of the associated memory devices (for example, ROM, fixed or removable memory) and, when ready to be utilized, loaded in part or in whole (for example, into RAM) and implemented by a CPU. Such software could include, but is not limited to, firmware, resident software, microcode, and the like.

A data processing system suitable for storing and/or executing program code will include at least one processor <NUM> coupled directly or indirectly to memory elements <NUM> through a system bus <NUM>. The memory elements can include local memory employed during actual implementation of the program code, bulk storage, and cache memories <NUM> which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during implementation.

Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, and the like) can be coupled to the system either directly or through intervening I/O controllers.

Network adapters <NUM> may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

As used herein, including the claims, a "server" includes a physical data processing system (for example, system <NUM> as shown in <FIG>) running a server program. It will be understood that such a physical server may or may not include a display and keyboard.

One or more embodiments can be at least partially implemented in the context of a cloud or virtual machine environment, although this is exemplary and non-limiting. Reference is made back to <FIG> and accompanying text.

It should be noted that any of the methods described herein can include an additional step of providing a system comprising distinct software modules embodied on a computer readable storage medium; the modules can include, for example, any or all of the appropriate elements depicted in the block diagrams and/or described herein; by way of example and not limitation, any one, some or all of the modules/blocks and or sub-modules/sub-blocks described. The method steps can then be carried out using the distinct software modules and/or sub-modules of the system, as described above, executing on one or more hardware processors such as <NUM>. Further, a computer program product can include a computer-readable storage medium with code adapted to be implemented to carry out one or more method steps described herein, including the provision of the system with the distinct software modules.

One example of user interface that could be employed in some cases is hypertext markup language (HTML) code served out by a server or the like, to a browser of a computing device of a user. The HTML is parsed by the browser on the user's computing device to create a graphical user interface (GUI).

Claim 1:
A method comprising:
training, using at least one processor, a supervised discriminator (<NUM>) to detect bio-markers in an audio sample dataset;
training, using the at least one processor, a denoising autoencoder (<NUM>) to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample of the audio sample dataset, wherein the training of the denoising autoencoder to learn the latent space that is used to reconstruct the output audio sample is performed by minimizing a KL-divergence based reconstruction error loss plus a fidelity term;
training, using the at least one processor, a conditional auxiliary generative adversarial network (GAN) (<NUM>) comprising the supervised discriminator (<NUM>) and the autoencoder (<NUM>) as generator to generate the output audio sample with the same fidelity as the input audio sample, wherein the output audio sample is void of the bio-markers; and
deploying the conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder in an audio processing system.