Patent Description:
The disclosure herein generally relates to the field of industrial machine inspection, and, more particularly, to method and system for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL).

In industrial inspection scenarios, early detection of machine faults is extremely important to prevent significant damage resulting in economic losses. Acoustic signals provide primary indications of machine health, studying the acoustic signals is imperative for detection of the machine faults. Another advantage is that the acoustic signals can be acquired unobtrusively using microphones. However, the acoustic signals captured in an industrial plant is mostly corrupted by interference and background noise due to multiple machines operating simultaneously. Document <NPL>" discloses that acoustic-based analysis has been widely used for the maintenance and operation of industrial machines. However, interferences and background noise highly contaminate the observed acoustic signal. The document presents a two-stage multichannel source separation technique for improved separation and robust anomaly detection. Beamforming is applied in the first stage to provide separation at a coarser level. Sequential transform learning is employed in the second stage to learn the dynamics of the time-varying source signal for more refined source separation. The separated machine sounds are analyzed for anomaly using a simple template matching approach. Results obtained using the MIMII dataset indicate that the proposed two-stage method provides an average improvement of <NUM> dB in signal-to-noise ratio and <NUM> % in accuracy when compared to the best-performing state-of-the-art methods for source separation and anomaly detection, respectively.

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. A first aspect of the invention refers to a method for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL) as defined in claim <NUM>.

In another aspect, a system for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL) is as defined in claim <NUM>.

In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause a method for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL) is as defined in claim <NUM>.

In accordance with an embodiment of the present invention, the source specific dictionary corresponding to each of the plurality of spatially distributed acoustic sources is learnt as defined in claim <NUM>.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention.

While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible as long as they don't depart from the scope of the invention as defined by the appended claims.

Acoustic signals originating from a machine are considered as one of the most important and early indicators of machine health. However, the acoustic signals acquired in an industrial setting is highly corrupted by interferences and background noise. Hence, there is a need to reduce this interference and the background noise so that the acoustic signals can be separated for improved anomaly detection. In practical application scenarios, information about the acoustic signals of all possible anomalous machine sounds is rarely available during training time.

In literature, an Autoencoder (AE) based architecture is used, where model is trained with the acoustic signals corresponding to normal machine sounds and anomaly scores are computed based on reconstruction error. These works make use of the publicly available Malfunctioning Industrial Machine Investigation and Inspection (MIMII) data where the acoustic signal corresponding to a single machine is present at a time, and that is analyzed for the anomaly detection. However, they do not handle composite mixtures of the acoustic signals where multiple sources are operating simultaneously, which is a usual case in real-world application scenarios. To handle the composite mixtures of the acoustic signals in literature (e.g., "<NPL>. ") a neural network based multi-object acoustic anomaly detection approach called as Information-Abstraction-Net (IA-Net) is used. It utilized mixtures synthesized using MIMII data. However, this work considered a single channel mixture that limits the scalability to a complicated multi-source scenario.

Beamforming is a well-known multi-channel source separation technique for spatially distributed acoustic sources. For wideband signals, like the acoustic signals, source separation with the beamforming alone is not efficient as beamwidth depends on frequency and it is not uniform across the entire wideband. At low frequencies beam will be wide and at high frequencies grating lobes will appear as it may violate Nyquist inter-element separation criterion allowing signals from non-desired directions. Hence, in the datasets like MIMII, a good performance using the beamforming is expected only in a mid-frequency region.

Apart from the beamforming, other multi-channel Signal Processing (SP) based Blind Source Separation (BSS) techniques such as, Independent Component Analysis (ICA), Nonnegative Matrix Factorization also exist in literature. Deep learning-based techniques have also been explored in literature for supervised BSS due to their ability to model complex functions. However, they are computationally intensive and require massive amount of labeled data for training that may not be feasible in the practical application scenarios.

Embodiments herein provide a method and system for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL). The method considers a multi-channel mixture synthesized by combining the acoustic signals from different machine sound sources to mimic a real factory setting. The present disclosure utilizes the DAS-BF and the DL based approach for the multi-channel source separation to separate the acoustic signals corresponding to the different machine sound sources that are further analyzed to detect anomalies.

The present disclosure is a two-stage approach for machine anomaly detection. In first stage, separation of the acoustic signals corresponding to the machine sound sources is performed at a coarser level by using the well-known computationally lightweight DAS-BF. Subsequently, dictionaries pre-trained using the acoustic signals of the individual machine sound sources are utilized for more refined source separation. The DL provides a data-driven paradigm for learning compact sparse representation of the acoustic signals and has been used successfully for signal processing, image processing and computational imaging. Hence, the DL is used in the present disclosure to learn representation of the acoustic signal for a source separation task. Further the separated sources are analyzed in second stage to detect anomalies by studying the deviation of the separated sources from a corresponding normal machine sound template. Experimental results obtained with the MIMII dataset demonstrate the potential of the disclosed method compared to other state-of-the-art methods for the machine anomaly detection.

<FIG> illustrates an exemplary system <NUM> for acoustic based industrial machine inspection using the DAS-BF and the DL, in accordance with some embodiments of the present disclosure. In an embodiment, the system <NUM> may also be referred as acoustic system. In an embodiment, the system <NUM> includes one or more hardware processors <NUM>, communication interface device(s) or input/output (I/O) interface(s) <NUM> (also referred as interface(s)), and one or more data storage devices or memory <NUM> operatively coupled to the one or more hardware processors <NUM>. The one or more processors <NUM> may be one or more software processing components and/or hardware processors.

Referring to the components of the system <NUM>, in an embodiment, the processor (s) <NUM> can be the one or more hardware processors <NUM>. In an embodiment, the one or more hardware processors <NUM> can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) <NUM> is/are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system <NUM> can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices (e.g., smartphones, tablet phones, mobile communication devices, and the like), workstations, mainframe computers, servers, a network cloud, and the like.

The I/O interface device(s) <NUM> can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.

The memory <NUM> may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, a database <NUM> is comprised in the memory <NUM>, wherein the database <NUM> comprises information on a multi-channel acoustic mixed signal, a plurality of spatially distributed acoustic sources, a plurality of beamformed source signals, a plurality of Mel-spectrograms, a plurality of separated acoustic source signals, the normal machine sound template, and a threshold value. The memory <NUM> further comprises a plurality of modules (not shown for various technique(s) such as the DAS BF, DL and the like. The memory <NUM> further comprises modules (not shown) implementing techniques such as Alternating Minimization (AM) approach, Iterative Soft Thresholding Algorithm (ISTA), Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), Mean-Square-Error (MSE) and, Signal-to-Noise Ratio (SNR). The above-mentioned technique(s) are implemented as at least one of a logically self-contained part of a software program, a self-contained hardware component, and/or, a self-contained hardware component with a logically self-contained part of a software program embedded into each of the hardware component (e.g., hardware processor <NUM> or memory <NUM>) that when executed perform the method described herein. The memory <NUM> further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory <NUM> and can be utilized in further processing and analysis.

<FIG> depicts overview of the architecture of the system for the acoustic based industrial machine inspection using the DAS-BF and the DL, according to some embodiments of the present disclosure. The system <NUM> in <FIG> presents a two-stage framework that combines the DAS-BF and the DL for carrying out multichannel source separation followed by machine anomaly detection. In the first stage, the DAS-BF is employed to estimate individual acoustic sources at the coarser level. Subsequently, source specific dictionaries are learned that are used to obtain clean sources from a composite mixture signal. With the robust source estimation in place, a simple template matching approach is employed in the second stage to detect the anomalies effectively.

The system <NUM> in <FIG> includes components such as, microphone array with M-elements is configured to receive the multi-channel acoustic mixed signal from the plurality of spatially distributed acoustic sources, a Delay-and-Sum Beamforming is configured to generate the plurality of beamformed source signals from the multi-channel acoustic mixed signal, source separation block configured to identify the plurality of separated acoustic source signals associated with the spatially distributed acoustic sources using the plurality of learned dictionaries and coefficients associated with each of the spatially distributed acoustic sources, a change detection block is configured to analyze the plurality of separated acoustic source signals with the corresponding normal machine sound template that classifies each of the spatially distributed acoustic sources as one of faulty and normal based on the threshold value.

<FIG> and <FIG> with reference to <FIG> are flow diagrams illustrating a method <NUM> for the acoustic based industrial machine inspection using DAS-BF and DL using the system of <FIG>, in accordance with some embodiments of the present disclosure. In an embodiment, the system(s) <NUM> comprises one or more data storage devices or the memory <NUM> operatively coupled to the one or more hardware processors <NUM> and is configured to store instructions for execution of steps of the method by the one or more processors <NUM>. The steps of the method of the present disclosure will now be explained with reference to components of the system <NUM> of <FIG> and <FIG> and the flow diagram illustrated in <FIG> and <FIG>. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.

Referring to steps of <FIG>, at step <NUM> of the method <NUM>, via the microphone array controlled by the one or more hardware processors <NUM> receive, the multi-channel acoustic mixed signal, from the plurality of spatially distributed acoustic sources. The plurality of spatially distributed acoustic sources include solenoid valves, water pumps, industrial fans, slide rails and thereof. The microphone array is comprising of M elements. A plurality of acoustic signals from the corresponding plurality of spatially distributed acoustic sources combinedly forms the multi-channel acoustic mixed signal. The multi-channel acoustic mixed signal received from the plurality of spatially distributed acoustic sources comprises a plurality of acoustic source signals that are interfered with each other. Let s<NUM>,. , sN represent the plurality of spatially distributed acoustic sources that are simultaneously present in the multi-channel acoustic mixed signal and are captured by the microphone array, as shown in the testing phase block of <FIG>, in accordance with some embodiments of the present disclosure. The plurality of spatially distributed acoustic sources corresponds to machine sounds in the industrial setting. The acoustic signal wm of the multi-channel acoustic mixed signal received at mth array of the microphone array is expressed as: <MAT> where amn is an attenuation coefficient; τmn denotes an arrival lag of the spatially distributed acoustic source n, at the microphone m of the microphone array with respect to a common reference microphone of the microphone array; nm(t) is an additive zero mean Gaussian noise at time t; <NUM> ≤ m ≤ M; and N represents the plurality of spatially distributed acoustic sources.

The plurality of spatially distributed acoustic sources and their spatial directions are known, in accordance with some embodiments of the present disclosure. Hence the arrival lags corresponding to the plurality of spatially distributed acoustic sources are known. The arrival lags corresponding to the plurality of spatially distributed acoustic sources are estimated by using one of Estimation of signal parameters via rotational invariance technique (ESPRIT), multiple signal classification (MUSIC) algorithm and root-MUSIC algorithm. The disclosed method objective is to detect the anomalous spatially distributed acoustic sources, if any, from the plurality of spatially distributed acoustic sources, s<NUM>(t),s<NUM>(t),. ,sN(t), using the given M microphone signals w<NUM>(t), w<NUM>(t),. wM(t) corresponding to the microphone array, assuming M > N. The two-stage approach used in the disclosed method consisting of separation of the plurality of spatially distributed acoustic sources followed by the change detection to identify anomaly associated with the corresponding plurality of spatially distributed acoustic sources.

At step <NUM> of the method <NUM>, the one or more hardware processors obtain, a plurality of beamformed source signals, by feeding the multi-channel acoustic mixed signal to the DAS-BF. The multi-channel acoustic mixed signal received by each of the microphone in the microphone array corresponds to different known arrival lags depending on corresponding spatial locations of the plurality of spatially distributed acoustic sources. The multi-channel acoustic mixed signal is represented as w<NUM>, w<NUM>,. wM that is fed to the DAS-BF, generating plurality of beamformed source signals that are represented as wbf<NUM>,. , wbfN as shown in the testing phase block of <FIG>, in accordance with some embodiments of the present disclosure. The DAS-BF adds a time-shifted delayed signals of the multi-channel acoustic mixed signal received by the microphone array, based on direction of the plurality of spatially distributed acoustic sources, generating the plurality of beamformed source signals. For sn for n = <NUM>,. , N, the DAS-BF adds the time-shifted delayed signals as: <MAT>.

Addition of the time-shifted delayed signals results in a constructive superposition of the acoustic signal only in the direction of desired source of the plurality of spatially distributed acoustic sources, thereby enhancing the desired source sN of the plurality of spatially distributed acoustic sources. This enables separation of the multi-channel acoustic mixed signal at the coarser level.

At step <NUM> of the method <NUM>, the one or more hardware processors compute, the plurality of Mel-spectrograms Wbf<NUM>,. , WbfN corresponding to each of the plurality of beamformed source signals wbf<NUM>,.

Upon obtaining a plurality of Mel-spectrograms corresponding to each of the plurality of beamformed source signals, at step <NUM> of the method <NUM>, the one or more hardware processors <NUM> estimate, coefficients <MAT> associated with each of the spatially distributed acoustic sources, from each of the plurality of Mel-spectrograms and a source specific dictionary learnt for each of the spatially distributed acoustic sources. The source specific dictionary corresponding to each of the spatially distributed acoustic source and the associated coefficients are estimated in a training phase as shown in the training phase block of <FIG>, in accordance with some embodiments of the present disclosure.

The method <NUM> receives via the corresponding microphone array, a plurality of multi-channel acoustic source signals, from each of the plurality of spatially distributed acoustic sources. During the training phase, in accordance with some embodiments of the present disclosure, the microphone array receives the multi-channel acoustic signal when only one of the plurality of spatially distributed acoustic sources is operational, and the remaining spatially distributed acoustic sources are not operational to learn the source-specific dictionaries. This process is considered for all N sources.

A plurality of beamformed signals sbf<NUM>,. , sbfN are estimated by feeding the multi-channel acoustic source signal of each of the plurality of spatially distributed acoustic sources to the DAS-BF individually as shown in the training phase block of <FIG>, in accordance with some embodiments of the present disclosure. The plurality of Mel-spectrograms Sbf<NUM>, Sbf<NUM>. Sbfn corresponding to each of the plurality of beamformed signals are computed. Then a plurality of learned dictionaries for each of the plurality of Mel-spectrograms corresponding to the plurality of the beamformed signals are generated by using a dictionary learning formulation. The dictionary learning formulation comprises the source specific dictionary of the plurality of learned dictionaries and the coefficients, for each of the spatially distributed acoustic sources. An l<NUM>-norm sparsity on the coefficients is enforced, for each of the dictionary learning formulation associated with each of the spatially distributed acoustic sources. The source specific dictionary and the coefficients associated with each of the spatially distributed acoustic sources are obtained, by iteratively solving the dictionary learning formulation using Alternating Minimization (AM) approach.

The plurality of spatially distributed acoustic sources for n = <NUM>,. , N, the source specific dictionary Dn for each of the plurality of spatially distributed acoustic sources is learnt using the dictionary learning formulation is given as: <MAT> where Dn ∈ RT×K denotes the source specific dictionary learnt for the nth source of the plurality of spatially distributed acoustic sources containing K atoms and Zn ∈ RK×L denotes the learnt coefficients for the nth source of the plurality of spatially distributed acoustic sources, Sbfn ∈ RT×L represents the Mel-spectrogram for the nth source of the plurality of spatially distributed acoustic sources with T features of length L.

In general, it is noted that value of K ≫ T that results an overcomplete dictionary. Hence a sparsity constraint is imposed on Zn. By enforcing the l<NUM>-norm sparsity on the coefficients Zn, the dictionary learning formulation is given as: <MAT> where λ is a real positive number that controls trade-off between the sparsity in Zn and data fidelity term ( <MAT>). Data fidelity term minimizes the reconstruction error that is measured by squared difference between the nth source of the plurality of beamformed signal and the associated dictionary learning solution DnZn.

The equation (<NUM>) is solved for Dn and Zn using an Alternating Minimization (AM) approach as: <MAT> <MAT>.

By using equation (<NUM>), an update for Dn is obtained by using a least squares approach, and an update for Zn is obtained using an Iterative Soft Thresholding Algorithm (ISTA)approach. The solution for the ISTA approach is expressed as: <MAT>.

The learnt source specific dictionary and the coefficients are updated iteratively based on the obtained updates of the Dn, and the Zn, until the objective function given in equation (<NUM>) of the dictionary learning formulation converges for each of the plurality of spatially distributed acoustic sources. The coefficients associated with each of the spatially distributed acoustic sources are estimated by using one of techniques such as ISTA, Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP).

Once the source-specific dictionaries are learnt in the training phase, the coefficients <MAT> are calculated for n = <NUM>,. , N for the plurality of beamformed source signals for estimating the plurality of separated acoustic source signals Ŝ<NUM>. ŜN as shown in the testing phase of <FIG>, in accordance with some embodiments of the present disclosure. For the nth source of the plurality of spatially distributed acoustic sources, with the beamformed source signal Wbfn and the learnt source specific dictionary Dn, the sparse solution <MAT> can be obtained by: <MAT>.

At step <NUM>, the one or more hardware processors <NUM> estimate, by the one or more hardware processors, the plurality of separated acoustic source signals associated with the spatially distributed acoustic sources, using the learnt source specific dictionary and coefficients associated with each of the plurality of spatially distributed acoustic sources. The nth separated acoustic source signal of the plurality of separated acoustic source signal is estimated as: <MAT>.

In the similar way, the plurality of separated acoustic source signals corresponding to the plurality of spatially distributed acoustic sources are estimated.

Upon estimating the plurality of separated acoustic source signals, at step <NUM> the one or more hardware processors <NUM> analyze the plurality of separated acoustic source signals with the corresponding normal machine sound template for each of the plurality of spatially distributed acoustic sources, using the threshold value. A deviation beyond the threshold value is indicated as the anomaly, and the threshold values corresponding to each of the plurality of spatially distributed acoustic sources are empirically calculated. Once the plurality of separated acoustic source signals are estimated, they are analyzed for the anomaly by observing the change between the plurality of separated acoustic source signals and their corresponding normal machine sound template using one of Mean-Square-Error (MSE) and Signal-to-Noise Ratio (SNR) approach.

The system <NUM> and method of the present disclosure is evaluated using the available MIMII dataset for robust anomaly detection. This dataset contains both the normal acoustic signals and anomalous acoustic signals captured from four different machines, namely, solenoid valves, water pumps, industrial fans, and slide rails, operating in a real factory environment. The experimental setup employs a circular microphone array of eight elements (channels), acoustic signals are sampled at <NUM> with the different machines spatially located at <NUM>, <NUM>, <NUM>, <NUM> degrees. The dataset contains multiple sound files (<NUM> each) for each of the spatially distributed acoustic sources with factory noise added at different signal-to-noise ratio (SNR) levels to mimic a real factory scenario. Although the data contains seven different product models for each of the plurality of spatially distributed acoustic sources, the proposed disclosure considers one product model (Model ID: <NUM>) for each of the spatially distributed acoustic sources type with <NUM> dB SNR for performance evaluation. Model ID: <NUM> contains a total of <NUM> normal and <NUM> anomalous sound files for the plurality of spatially distributed acoustic sources. In literature (e. g, "<NPL>"), multi-channel composite mixtures are synthesized by adding together (channel-wise) the normal and abnormal sounds from plurality of spatially distributed acoustic sources to mimic the real factory environment where the plurality of spatially distributed acoustic sources operates simultaneously.

The system <NUM> and method of the present disclosure is compared against the IA-Net method in literature (e. g, "<NPL>")) that considers a signal channel mixture. For reference, results with the MIMII baseline anomaly detection methods based on Autoencoder (AE) and its deep variant, Dense AE are also provided. It is noted that both the AE baseline techniques consider a single channel and a single source. They employ separate AEs that are trained for individual acoustic signals for the anomaly detection. Additionally, the system <NUM> and method of the present disclosure is compared with three state-of-the-art Source Separation (SS) methods namely, Multichannel Variational Autoencoder (MVAE), Fast Multichannel Nonnegative Matrix Factorization (FastMNMF) and Random Directions (Randdir), in accordance with some embodiments of the present disclosure. While the MVAE is completely a data-driven approach, the FastMNMF is based on traditional signal processing techniques and the Randdir considers a probabilistic optimization framework. Similar to the present disclosure, for fair comparison, these methods use DAS-BF signals as an input in the first stage followed by the same change detection in the second stage for the anomaly detection.

MVAE and FastNMF consider spectrograms extracted from each of the plurality of beamformed source signals that are stacked together and fed as input to these methods. While the Randdir works directly on the stacked plurality of beamformed source signals. In the present disclosure the plurality of Mel-spectrograms are computed using a frame size of <NUM>, a hop size of <NUM>, and <NUM> Mel-filters for each of the plurality of beamformed source file. Four frames of the plurality of Mel-spectrograms are combined to create an input feature vector, T=<NUM>. The dictionaries are learnt for each of the plurality of beamformed signals using the normal machine sound template in the training phase. The value of λ = <NUM> and K = <NUM> are obtained using a grid search for the plurality of spatially distributed acoustic sources. The disclosed method <NUM> is run for <NUM> iterations in the training phase. Here, <NUM>% of normal acoustic sound files for each of the plurality of spatially distributed acoustic sources are used in the training phase. While the remaining <NUM>% synthesized normal mixture files are used for in the testing phase. The faulty sound mixtures are synthesized using all the anomalous acoustic sound files. It is ensured that the faulty mixture contains sound of only one faulty machine at a time. The change detection module makes use of MSE for computing change between the plurality of separated acoustic source signals and the corresponding normal machine sound template to identify the change. Any change observed beyond a threshold is indicated as an anomaly. It is noticed that the thresholds are empirically calculated for each plurality of spatially distributed acoustic sources and tuned separately for each method.

TABLE I summarizes the performance of different methods for the anomaly detection in terms of accuracy and F1 score. To demonstrate the effectiveness of the disclosed method for estimating the plurality of separated acoustic source signals associated with the spatially distributed acoustic sources, in addition to other source separation methods, results with the DAS-BF alone are also presented. From the TABLE <NUM> it is observed that, unlike other approaches that seem to provide high accuracy compared to the F1 score for the individual acoustic sources, the present disclosure performs consistently well in both metrics. This indicates that the disclosed two-stage method is more robust and capable of detecting acoustic anomalies with less false positives. Even among different source separation techniques, the disclosed method provides the best performance, with FastMNMF being the second best. It is noticed that results with the DAS-BF technique alone are not good as the plurality of separated acoustic source signals are highly impacted by interfering sources and noise, making it challenging to identify the acoustic anomalies. More refined separation using the DL approach resulted in the better anomaly detection. The results also present the improvement in detection accuracy observed when the multi-channel acoustic mixed signal is utilized compared to a single channel acoustic mixture considered in the IA-Net. Moreover, it is also clear from that table that the disclosed two-stage approach performs even better than the single source anomaly detection baseline methods. It is noticed that, unlike in literature (e.g., "<NPL>. ") and (e.g., "<NPL>. ") a mixture of multiple sources is present in most of the practical industrial applications. The results show the ability of the disclosed two-stage approach for estimating the plurality of separated acoustic source signals that helps in the robust anomaly detection under such challenging multi-source scenarios.

It should be noted and understood that improvements and modifications of the embodiments described above may be made, within the scope of the appended claims.

The present disclosure herein addresses estimation of the plurality of separated acoustic source signals from the multi-channel acoustic mixed signal in the industrial setting. The disclosed method presents the two-stage approach for the anomaly detection using the multi-channel acoustic mixed signal. In the first stage, separation of the plurality of acoustic signals corresponding to the spatially distributed acoustic sources is performed at the coarser level by using a well-known computationally lightweight DAS-BF. Subsequently, the dictionaries pre-trained using the plurality of acoustic signals of the individual source machines are utilized for generating the plurality of separated acoustic source signals. The generated plurality of separated acoustic source signals are analyzed for the anomaly detection by comparing them with the corresponding normal machine sound template.

Claim 1:
A processor implemented method (<NUM>), the method comprising:
receiving (<NUM>), via a microphone array controlled by one or more hardware processors, a multi-channel acoustic mixed signal, from a plurality of spatially distributed acoustic sources, wherein the multi-channel acoustic mixed signal comprises a plurality of acoustic source signals interfered with each other, received from the plurality of spatially distributed acoustic sources,
obtaining (<NUM>), by the one or more hardware processors, a plurality of beamformed source signals, by feeding the multi-channel acoustic mixed signal to a Delay-and-Sum beamforming, DAS-BF;
computing (<NUM>), by the one or more hardware processors, a plurality of Mel-spectrograms corresponding to each of the plurality of beamformed source signals;
estimating (<NUM>), by the one or more hardware processors, coefficients associated with each of the plurality of spatially distributed acoustic sources, from each of the plurality of Mel-spectrograms and a source specific dictionary learnt for each of the plurality of spatially distributed acoustic sources;
estimating (<NUM>), by the one or more hardware processors, a plurality of separated acoustic source signals associated with the plurality of spatially distributed acoustic sources, using the learnt source specific dictionary and the coefficients associated with each of the plurality of spatially distributed acoustic sources; and
analyzing (<NUM>), by the one or more hardware processors, the plurality of separated acoustic source signals with a normal machine sound template corresponding to each of the plurality of spatially distributed acoustic sources, using a threshold value, wherein a deviation beyond the threshold value is indicated as an anomaly.