METHOD AND APPARATUS FOR AUTOMATIC COUGH DETECTION

A method for identifying cough sounds in an audio recording of a subject including: operating at least one electronic processor to identify potential cough sounds in the audio recording; operating the at least one electronic processor to transform one or more of the potential cough sounds into corresponding one or more image representations; operating the at least one electronic processor to apply the one or more image representations to a representation pattern classifier trained to confirm that a potential cough sound is a cough sound or is not a cough sound; and operating the at least one electronic processor to flag one or more of the potential cough sounds as confirmed cough sounds based on an output of the representation pattern classifier.

RELATED APPLICATIONS

The present application claims priority from Australian provisional patent application No. 2019904755 filed 16 Dec. 2019, the disclosure of which is hereby incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to a method and apparatus for processing subject sounds for automatic detection of cough sounds therein.

BACKGROUND

Any references to methods, apparatus or documents of the prior art are not to be taken as constituting any evidence or admission that they formed, or form part of the common general knowledge.

It is known to electronically process subject sounds to predict the presence of respiratory maladies. Where a symptom of the malady is coughing in the subject then it is important to be able to identify segments of the subject sounds that contain coughs, as opposed to background noise for example.

A number of approaches to identifying cough segments of patient sounds are known in the prior art. For example, in WO2013/142908 by Abeyratne at al. there is described a method for cough detection which involves determining a number of features for each of a plurality of segments of a subject's sound, forming a feature vector from those features and applying them to a pre-trained classifier. The output from the classifier is then processed to deem the segments as either “cough” or “non-cough”.

A more recent approach to identifying portions of subject sounds that contain coughs is described in WO 2018/141013 (sometimes called the “LW2” method herein) in which feature vectors from the subject sound are applied to two pre-trained neural nets being respectively trained for detecting an initial phase of a cough sound and a subsequent phase of a cough sound. The first neural net is weighted in accordance with positive training to detect the initial, explosive phase, and the second neural net is positively weighted to detect one or more post-explosive phases of the cough sound. In a preferred embodiment of the LW2 method the first neural net is further weighted in accordance with positive training in respect of the explosive phase and negative training in respect of the post-explosive phases. LW2 is particularly good at identifying cough sounds in a series of connected coughs.

The Inventors have noticed that a problem that can occur with prior art cough identification methods is that they may have undesirably low specificity which means that they identify sound segments as being cough sounds when in fact they are not. Such false positive detection may make those methods infeasible for long term use in high background noise environments where the number of non-cough events in the subject sound recording is much greater than the number of cough events.

It would be desirable if a method and apparatus were provided that can reduce the number of false positives.

SUMMARY OF THE INVENTION

A method for identifying cough sounds in an audio recording of a subject comprising:operating at least one electronic processor to identify potential cough sounds in the audio recording;operating the at least one electronic processor to transform one or more of the potential cough sounds into corresponding one or more image representations;operating the at least one electronic processor to apply said one or more image representations to a representation pattern classifier trained to confirm that a potential cough sound is a cough sound or is not a cough sound; andoperating the at least one electronic processor to flag one or more of the potential cough sounds as confirmed cough sounds based on an output of the representation pattern classifier.

In an embodiment the method includes, operating said processor to transform the one or more sounds into the image representations wherein the image representations relate frequency and time.

In an embodiment, the one or more image representations comprise spectrograms.

In an embodiment, the one or more image representations comprise mel-spectrograms.

In an embodiment, the method includes, operating said processor to identify the potential cough sounds as cough audio segments of the audio recording by using first and second cough sound pattern classifiers trained to respectively detect initial and subsequent phases of cough sounds.

In an embodiment, the one or more image representations have a dimension of N×M pixels and are formed by said processor processing N windows of each of the cough audio segments wherein each of the N windows is analyzed in M frequency bins.

In an embodiment, each of the N windows overlaps with at least one other of the N windows.

In an embodiment, length of the windows is proportional to length of its associated cough audio segment.

In an embodiment the method includes operating said processor to calculate a Fast Fourier Transform (FFT) and a power value per frequency bin to arrive at a corresponding pixel value of the corresponding image representation of the or more image representations.

In an embodiment the method includes, operating said processor to calculate a power value per frequency bin in the form of M power values, being power values for each of the M frequency bins.

In an embodiment, the M frequency bins comprise M mel-frequency bins, the method including operating said processor to concatenate and normalize the M power values to thereby produce the corresponding image representation in the form of a mel-spectrogram image.

In an embodiment, the image representations are square and wherein M equals N.

In an embodiment, the representation pattern classifier comprises a neural network.

In an embodiment, the neural network is a convolutional neural network (CNN).

In an embodiment the method includes, operating said processor to compare a probability value comprising, or based upon, an output of the representation pattern classifier with a predetermined threshold value.

In an embodiment the method includes, operating said processor to flag one or more of the potential cough sounds as confirmed cough sounds upon the probability value exceeding the predetermined threshold value.

In an embodiment the method includes, operating said processor to flag the confirmed cough sounds by recording begin and end times of the corresponding cough audio segment as being begin and end times of a confirmed cough sound.

In an embodiment the method includes, operating said processor to generate a screen on a display responsive to said processor, the screen indicating the number of potential cough sounds processed and the number of confirmed cough sounds.

According to a further apparatus there is provided an apparatus for identifying cough sounds in a subject comprising:an audio capture arrangement configured to store a digital audio recording of a subject in an electronic memory;a sound segment-to-image representation assembly arranged to transform pre-identified potential cough sounds into corresponding image representations;a representation pattern classifier in communication with the sound segment-to-image representation assembly that is configured to process the image representations to thereby produce a signal indicating a probability of the image representations corresponding to the pre-identified potential cough sounds being a confirmed cough sound.

In an embodiment the apparatus includes, or more cough sound classifiers trained to identify portions of the digital audio recording to thereby produce the pre-identified potential cough sounds.

In an embodiment, the one or more cough sound classifiers comprise a first cough sound pattern classifier and a second cough sound pattern classifiers trained to respectively detect initial and subsequent phases of cough sounds.

In an embodiment, the first cough sound pattern classifier and the and second cough sound pattern classifier each comprise neural networks.

In an embodiment, the sound segment-to-image representation assembly is arranged to transform the pre-identified potential cough sounds into corresponding image representations comprising spectrograms.

In an embodiment, the sound segment-to-image representation assembly is arranged to transform the pre-identified potential cough sounds into corresponding image representations by calculating a Fast Fourier Transform and a power per bin for M to the pre-identified potential cough sounds.

In an embodiment, the sound segment-to-image representation assembly is arranged to transform the pre-identified potential cough sounds into spectrograms

In an embodiment, the spectrograms comprise mel-spectrograms.

In an embodiment, the apparatus includes at least one electronic processor in communication with the electronic memory, wherein the processor is configured by instructions stored in the electronic memory to implement the sound segment-to-image representation assembly.

In an embodiment, the at least one electronic processor is configured by instructions stored in the electronic memory to implement the representation pattern classifier.

In an embodiment, the at least one electronic processor is configured by instructions stored in the electronic memory to implement the at least one cough sound pattern classifier arranged to identify the potential cough sounds.

According to a further aspect of the present invention there is provided a method for training a pattern classifier to confirm a potential cough sound as a confirmed cough sound from a sound recording of the subject, the method comprising:transforming cough sounds and non-cough sounds of subjects into corresponding image representations;training the pattern classifier to produce an output predicting that a potential cough sound is a confirmed cough sound in response to application of image representations corresponding to confirmed cough sounds and to produce an output predicting that a potential cough sound is not a cough sound in response to application of image representations corresponding to non-cough sounds.

According to another aspect there is provided a method for identifying cough sounds in an audio recording of a subject including transforming potential cough sounds in the audio recording into corresponding image representations and then applying the image representations to a pre-trained classifier and based on output from the pre-trained classifier flagging the potential cough sounds as confirmed cough sounds or not.

According to a further aspect there is provided an apparatus for processing potential cough sounds identified in an audio recording of a subject, the apparatus including at least one electronic processor in communication with a digital memory storing instructions to configure said processor to implement the method.

According to another aspect of the present invention there is provided a computer readable media bearing tangible, non-transitory machine readable instructions for one or more processors to implement a method for confirming a potential cough sound to be a confirmed cough sound based on an image representation of the potential cough sound.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG.1presents a flowchart of a method according to a preferred embodiment of the present invention for automatic cough detection.

A hardware platform that is configured to implement the method comprises a cough identification machine. The machine may be a desktop computer or a portable computational device such as a smartphone that contains at least one processor in communication with an electronic memory that stores instructions that specifically configure the processor in operation to carry out the steps of the method as will be described. It will be appreciated that it is impossible to carry out the method without the specialized hardware, i.e. either a dedicated machine or a machine that is comprised of specially programmed one or more processors. Alternatively, the machine may be implemented as a dedicated assembly that includes specific circuitry to carry out each of the steps that will be discussed. The circuitry may be largely implemented using a Field Programmable Gate Array (FPGA) configured according to a Hardware Descriptor Language (HDL) or Verilog specification.

FIG.2is a block diagram of an apparatus comprising a cough identification machine51that, in the presently described embodiment, is implemented using the one or more processors and memory of a smartphone. The cough identification machine51includes at least one processor53that accesses an electronic memory55. The electronic memory55includes an operating system58such as the Android operating system or the Apple iOS operating system, for example, for execution by the processor53. The electronic memory55also includes a cough identification software product or “App”56according to a preferred embodiment of the present invention. The cough identification App56includes instructions that are executable by the processor53in order for the cough identification machine51to process sounds from a subject52and present an identification of coughs to a clinician54by means of LCD touch screen interface61. The App56includes instructions for the processor53to implement a pattern classifier such as a trained predictor or decision machine, which in the presently described preferred embodiment of the invention comprises a specially trained Convolutional Neural Network (CNN)63.

The processor53is in data communication with a plurality of peripheral assemblies59to73, as indicated inFIG.2, via a data bus57which is comprised of metal conductors along which digital signals200are conveyed between the processor and the various peripherals. Consequently, if required the cough identification machine51is able to establish voice and data communication with a voice and/or data communications network81via WAN/WLAN assembly73and radio frequency antenna79. The machine also includes other peripherals such as Lens & CCD assembly59which effects a digital camera so that an image of subject52can be captured if desired. A LCD touch screen interface61is provided that acts as a human-machine interface and allows the clinician54to read results and input commands and data into the machine51. A USB port65is provided for effecting a serial data connection to an external storage device such as a USB stick or for making a cable connection to a data network or external screen and keyboard etc. A secondary storage card64is also provided for additional secondary storage if required in addition to internal data storage space facilitated by memory55. Audio interface71couples a microphone75to data bus57and includes anti-aliasing filtering circuitry and an Analog-to-Digital sampler to convert the analog electrical waveform40from microphone75(which corresponds to subject sound wave39) to a digital audio signal50(shown stored in memory inFIG.2and shown graphically inFIG.5) that can be stored in memory55and processed by processor53. The audio interface71is also coupled to a speaker77. The audio interface71includes a Digital-to-Analog converter for converting digital audio into an analog signal and an audio amplifier that is connected to speaker71so that audio recorded in memory55or secondary storage64can be played back for listening by clinician54. It will be realized that the microphone75and audio interface71along with processor53programmed with App56comprise an audio capture arrangement that is configured for storing a digital audio recording50of subject52in an electronic memory such as memory55or secondary storage64.

The cough identification machine51is programmed with App56so that it is configured to operate as a machine for identifying cough segments in the recording of the subject sound.

As previously discussed, although the cough identification machine51that is illustrated inFIG.2is provided in the form of smartphone hardware that is uniquely configured by App56it might equally make use of some other type of computational device such as a desktop computer, laptop, or tablet computational device or even be implemented in a cloud computing environment wherein the hardware comprises a virtual machine that is specially programmed with App56. Furthermore, a dedicated cough identification machine might also be constructed that does not make use of a general purpose processor. For example, such a dedicated machine may have an audio capture arrangement including a microphone and analog-to-digital conversion circuitry configured to store a digital audio recording of the subject in an electronic memory. The machine further includes a potential cough sound identification assembly in communication with the memory and arranged to process the digital audio recording to thereby identify segments of the digital audio potentially containing cough sounds, i.e. potential cough sounds. Preferably the potential cough sound identification assembly is arranged to implement the LW2 method of WO2018/141013. A sound segment to image representation assembly may be provided that transforms identified cough sound segments into image representations. The dedicated machine further includes a hardware implemented pattern classifier to produce a signal indicating the potential cough sound as being either a confirmed cough sound or a non-cough sound.

An embodiment of the procedure that cough identification machine51uses to identify cough segments in a recording of subject52, and which comprises instructions that make up App56is illustrated in the flowchart ofFIG.1and will now be described in detail.

Initially clinician54, or another carer or even subject39, selects App56from an app selection screen generated by OS58on LCD touchscreen interface61. In response to that selection the processor53displays a screen such as screen82ofFIG.3to prompt the clinician54to operate machine51to commence recording sound39from subject52via microphone75and audio interface71. The audio interface71converts the sound into digital signals200which are conveyed along bus57and recorded as one or more digital files50by processor53in memory55and/or secondary storage SD card64. In the presently described preferred embodiment the recording should proceed for a duration that is sufficient to include a number of cough sounds of the subject52to be present in the sound recording.

At box10processor53identifies potential cough sounds (PCSs) in the audio sound files50. In a preferred embodiment of the invention the App56includes instructions that configure processor53to implement a first cough sound pattern classifier (CSPC 1)62aand a second cough sound pattern classifier (CSPC 2)62b,each preferably comprising neural networks trained to respectively detect initial and subsequent phases of cough sounds. Thus, in that preferred embodiment the processor53identifies the PCSs using the LW2 method that is described in the previously mentioned international patent application publication WO 2018/141013, the disclosure of which is hereby incorporated herein in its entirety by reference. Other methods for identifying potential cough sounds may alternatively be used at box10, for example the methods described in the previously mentioned international patent publication WO2013/142908 by Abeyratne at al might also be used.

FIG.4is a graph showing a portion of the recorded sound wave50from subject52. Application of the method described in WO 2018/141013 involves applying features of the sound wave to two trained neural networks which are respectively trained to recognize a first phase and a second phase of a cough sound. The output of the first neural network is indicated as line54inFIG.4and comprises a signal that represents the likelihood of a corresponding portion of the sound wave being a first phase of a cough sound. The output of the second neural network is indicated as line52inFIG.4and comprises a signal that represents the likelihood of a corresponding portion of the sound wave being a subsequent phase of the cough sound. Based on the outputs54and52of the first and second trained neural networks processor53identifies two Potential Cough Sounds66aand66bwhich are located in segments68aand68b.

At box12the processor53sets a variable Current PCS to the first PCS that has been previously identified, i.e. “pre-identified” at box10.

At box14the processor53transforms the pre-identified PCS that is stored in the Current PCS variable to produce a corresponding image representation76which it stores in either memory55or secondary storage64.

This image representation may comprise, or be based on, a spectrogram of the Current Cough Sound portion of the digital audio file. Possible image representations include mel-frequency spectrogram (or “mel-spectrogram”), continuous wavelet transform, and derivatives of these representations along the time dimension, also known as delta features. Consequently, the image representations relate frequency, for example on a vertical axis, with time for example on a horizontal axis, over the duration of the PCS.

An example of one particular implementation of box14is depicted inFIG.5. Initially the processor53identifies two Potential Cough Sounds (PCS)66a,66bin the digital sound file50.

Processor53identifies the Potential Cough Sounds66aand66bas separate cough audio segments68aand68b.Each of the separate cough audio segments68aand68bare then divided into N, in the present example N=5, equal length overlapping windows72a1, . . . ,72a5and72b1, . . . ,72b5. For a shorter cough segment, e.g. cough segment68bwhich is somewhat shorter than cough segment68a,the overlapping windows72bthat are used to segment section68bare proportionally shorter to the overlapping windows72athat are used to segment section68a.

Processor53then calculates a Fast Fourier Transform (FFT) and a power value per mel-bin, for M=5 bins for each of the N=5 windows, to arrive at corresponding pixel values. Machine readable instructions that configure a processor to perform these operations on the sound wave are included in App56. Such instructions are publicly available, for example at: https://librosa.github.io/librosa/_modules/librosa/core/spectrum.html (retrieved 11 Dec. 2019).

Processor53concatenates and normalizes the values stored in the spectrograms74aand74bto produce corresponding Square Mel-Spectrogram images76aand76brepresenting cough sounds66aand66brespectively. Each of images76aand76bis an 8-bit greyscale M×N image where M=N.

N may be any positive integer value bearing in mind that at some N, depending on the sampling rate of the audio interface71, the cough image will contain all information present in the original audio, which is desirable. The number of FFT bins may need to be increased to accommodate higher N.

FIGS.6aand6bare Square Mel-spectrogram images of non-cough segments of the subject sound recording, obtained using the process described inFIG.5with N=M=224. In this image, time increases from left to right and frequency increases from bottom to top. Darker areas denote increased amplitude of the mel-frequency bin.

In contrastFIGS.7aand7bare Square Mel-spectrogram images of cough segments, e.g. one of segments68a,68b.

The images inFIGS.6ato7bhave been thresholded to convert them to black and white image for purposes of official publication of this patent specification.

Although it is convenient to use square representations that are N×M pixels derived from N segments, each analyzed for M frequency bins, where N=M, it is also possible to use rectangular representations where N is not equal to M provided that the CNN63has been trained using similarly dimensioned rectangular images.

From the discussion of box14it will be understood that processor53, configured by App56to perform the procedure of box14, comprises a sound segment-to-image representation assembly that is arranged to transform sound segments of the recording, previously identified as Potential Cough Sounds, e.g. at box10, into corresponding image representations.

Returning now toFIG.1, at box16processor53applies the image representation, for example image76ato a representation pattern classifier in the form of the trained convolutional neural network (CNN)63. The CNN63is trained to confirm whether or not the image representation of the Potential Cough Sound is indeed a cough sound i.e. a Confirmed Cough Sound (CCS). The CNN63comprises a representation pattern classifier that generates an output probability signal which ranges between 0 and 1 wherein 1 indicates a certainty that the Potential Cough Sound (PCS) is indeed a cough sound and thus a Confirmed Cough Sound and 0, which indicates that there is no likelihood of the PCS being a cough sound. A probability value p is arrived at box18from the output of the trained Neural Network (CNN) at box16. At box20the p value that has been determined at box18is compared to a threshold value stored in variable Threshold. The threshold value is preferably 0.5 so that the PCS is deemed to be a CCS provided that the p value indicates that the PCS is more than likely a CCS. Higher or lower threshold values may be used as desired depending on the requirements of the specific situation.

If p is greater than Threshold at box20then at box22processor53flags that the current PCS is a CCS, for example by recording the corresponding sound segment's begin and end times as being the begin and end times of a confirmed cough sound (CCS).

If the p value is not greater than Threshold then the PCS is not flagged as being a CCS. Control then proceeds to decision box24. At decision box24processor53checks if there are any more PCSs to be processed. If there are more PCSs, that were identified at box10, to be processed then at box26the Current PCS variable is set to the next identified PCS and control proceeds to box14where the previously described boxes14to22are repeated. If, at box24, there are no more PCSs to be processed then control proceeds to box28where processor53operates a display in the form of LCD Touch Screen Interface61, which is responsive to processor53, to display the screen78shown inFIG.8. Screen78presents the number of PCSs processed and the number that have been found to be CCSs. It also presents the start and end times for each CCS so that clinician54can listen to them via speaker77if desired.

FIG.9is a block diagram of a CNN training machine133implemented using the one or more processors and memory of a desktop computer configured according to CNN training Software140. CNN training machine133includes a main board134which includes circuitry for powering and interfacing to one or more onboard microprocessors (CPUs)135.

The main board134acts as an interface between microprocessors135and secondary memory147. The secondary memory147may comprise one or more optical or magnetic, or solid state, drives. The secondary memory147stores instructions for an operating system139. The main board134also communicates with random access memory (RAM)150and read only memory (ROM)143. The ROM143typically stores instructions for a startup routine, such as a Basic Input Output System (BIOS) or Unified Extensible Firmware Interface (UEFI) which the microprocessor135accesses upon start up and which preps the microprocessor135for loading of the operating system139. For example Microsoft Windows, and Ubuntu Linux Desktop are two examples of such an operating system.

The main board134also includes an integrated graphics adapter for driving display147. The main board133will typically include a communications adapter153, for example a LAN adaptor or a modem or a serial or parallel port, that places the server133in data communication with a data network.

An operator167of CNN training machine133interfaces with it by means of keyboard149, mouse121and display147.

The operator167may operate the operating system139to load software product140. The software product140may be provided as tangible, non-transitory, machine readable instructions159borne upon a computer readable media such as optical disk157for reading by disk drive152. Alternatively, it might also be downloaded via port153.

The secondary storage147also includes software product140, being a CNN training software product140according to an embodiment of the present invention. The CNN training software product140is comprised of instructions for CPUs135(or as alternatively and collectively referred to “processor135”) to implement the method that is illustrated inFIG.10.

Initially at box192ofFIG.10processor135retrieves a training subject audio dataset which in the presently described embodiment is comprised of 70,000 cough segments and non-cough segments. The metadata includes training labels, i.e., whether or not each segment is actually a cough or not.

At box196the processor135represents the non-cough events and the cough events as images in the same manner as has previously been discussed at box14ofFIG.1wherein Mel-spectrogram images are created to represent each potential cough sound (PCS).

At box198processor135transforms each image produced at box196to create additional training examples for subsequently training a convolutional neural net (CNN). This data augmentation step at box198is preferable because a CNN is a very powerful learner and with a limited number of training images it can memorize the training examples and thus over fit the model. The Inventors have discerned that such a model will not generalize well on previously unseen data. The applied image transformations include, but are not limited to, small random zooming, cropping and contrast variations.

At box200the processor135trains the CNN142on the augmented cough and non-cough images that have been produced at box198and the original training labels. Over fitting of the CNN142is further reduced by using regularization techniques such as dropout, weight decay and batch normalization.

One example of the process used to produce a CNN142is to take a pretrained ResNet model, which is a residual network containing shortcut connections, such as ResNet-18, and use the convolutional layers of the model as a backbone, and replace the final non-convolutional layers with layers that suit the cough identification problem domain. These include fully connected hidden layers, dropout layers and batch normalization layers. Information about ResNet-18 is available at: https://www.mathworks.com/help/deeplearning/ref/resnet18.html (retrieved 2 Dec. 2019), the disclosure of which is incorporated herein by reference. ResNet-18 is a convolutional neural network that is trained on more than a million images from the ImageNet database (http://www.image-net.org). The network is 18 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 224-by-224 pixels.

The Inventors have found that it is sufficient to fix the ResNet-18 layers and only train the new non-convolutional layers, however it is also possible to re-train both the ResNet-18 layers and the new non-convolutional layers to achieve a working model. A fixed dropout ratio of 0.5 is preferably used. Adaptive Moment Estimation (ADAM) is preferably used as an adaptive optimizer though other optimizer technique may also be used.

At box202the original (non-augmented) cough and non-cough images from box196are applied to the CNN142which is now trained to respond with probabilities for each.

The trained CNN is then distributed as CNN63as part of cough identification App56being CNN63.

To test the performance of the method ofFIG.1, the Inventors developed a dataset of 48471 coughs, and 19260 non coughs. The non-cough sounds in the dataset were specifically chosen from events that had been incorrectly flagged as a cough by the LW2 algorithm.

75% of that set was used to train the CNN142for Deep Cough ID and the remaining 25% (12225 coughs and 4707 non coughs) was used as a test set.

Using LW2, 12225 coughs (PCS) were identified, while 4,707 non-cough events were false positives (i.e. LW2 said these were coughs whereas further investigation revealed that they were not). When Deep Cough ID was used after LW2, 12223 coughs were identified (ie. 2 coughs were false negatives and incorrectly classified), and 4663 non-cough events were now correctly classified (rejected) and only 44 of these non-cough events were incorrectly classified as coughs.

A summary of the performance of the method ofFIG.1on the test set is set out in Table 1:

It will be observed from the above table that embodiments of the present invention result in an accuracy increase of over 25% over the prior art LW2 method that is the subject of international patent publication WO 2018/141013.

To recap, in one aspect a method is provided for identifying cough sounds, such as cough sounds66a,66bin an audio recording, such as digital sound file50, of a subject52. The method in this aspect involves operating at least one electronic processor53to identify potential cough sounds (box10ofFIG.1) in the audio recording52, for example by, but not limited to, using the LW2 procedure described in relation toFIG.4. The method also involves operating the electronic processor53to transform (box14ofFIG.1) one or more of the potential cough sounds into corresponding one or more image representations such as image representations76a,76b(FIG.5).

The electronic processor53is operated to apply the one or more image representations76a,76bto a representation pattern classifier63(FIG.2) trained to confirm (box18ofFIG.1) that a potential cough sound is a cough sound or is not a cough sound. The method includes operating the at least one electronic processor53to flag one or more of the potential cough sounds as confirmed cough sounds (box22FIG.1) based on an output of the representation pattern classifier63.

In another aspect an apparatus has been described for identifying cough sounds in a subject. The apparatus includes an audio capture arrangement, for example comprised of microphone75(FIG.2) and audio interface71along with processor53configured by App56to capture and store a digital audio recording50of subject52in an electronic memory such as memory55or secondary storage64.

The apparatus has a sound segment-to-image representation assembly arranged to transform pre-identified potential cough sounds into corresponding image representations. For example, the sound segment-to-image representation assembly may comprise processor53, configured by App56to perform the procedure of box14(FIG.1that is arranged to transform sound segments of the recording, previously identified as Potential Cough Sounds, e.g., at box10, into corresponding image representations.

The apparatus also includes a representation pattern classifier in communication with the sound segment-to-image representation assembly that is configured to process the image representations to thereby produce a signal indicating a probability of the image representations corresponding to the pre-identified potential cough sounds being a confirmed cough sound. The representation pattern classifier may be in the form of a trained convolutional neural network (CNN)63, which is trained to confirm whether or not the image representation of the Potential Cough Sound is indeed a cough sound i.e. a Confirmed Cough Sound (CCS).

In compliance with the statute, the invention has been described in language more or less specific to structural or methodical features. The term “comprises” and its variations, such as “comprising” and “comprised of” is used throughout in an inclusive sense and not to the exclusion of any additional features.

It is to be understood that the invention is not limited to specific features shown or described since the means herein described comprises preferred forms of putting the invention into effect. The invention is, therefore, claimed in any of its forms or modifications within the proper scope of the appended claims appropriately interpreted by those skilled in the art.

Throughout the specification and claims (if present), unless the context requires otherwise, the term “substantially” or “about” will be understood to not be limited to the value for the range qualified by the terms.

Any embodiment of the invention is meant to be illustrative only and is not meant to be limiting to the invention. Therefore, it should be appreciated that various other changes and modifications can be made to any embodiment described without departing from the spirit and scope of the invention.