Abstract

Nowadays, we use speech-enabled smart devices either to improve human-machine or humanhuman interaction. One of the primary tasks in these speech-enabled devices is speech enhancement. Speech enhancement is the extraction of the desired speech signal from the noisy and reverberant mixture signals recorded by the microphones. The performance of the speech-enabled devices relies significantly on the performance of the speech enhancement methods. Among these enhancement methods, beamformers and Time-Frequency (TF) mask-based methods are widely used. Beamformers are linear spatial filters that aim to boost the signal coming from a specific direction by appropriate configuration of the microphone array, and in doing so attenuates interfering signals from other directions. A source TF mask identifies the TF regions where the source is dominant and can be applied on the mixture TF representation to extract the desired source. Several beamformers and a few TF mask estimators estimate the azimuth and elevation angles called the Direction of Arrival (DoA) of the sources from the microphone data. Several other speech enhancement methods estimate the source TF masks. In all these methods, it is necessary that the DoA and the masks be accurately estimated for high quality of the enhanced signals. The thesis addresses the following problems: the problem of robust DoA and TF-mask estimation from the noisy and reverberant microphone signals and the problem of enhancement of a TF mask. Specifically, this thesis proposes two robust DoA estimators and a novel TF mask interpolation technique to improve the TF masks. Further, the efficacy of eigenvalue features for robust TF mask estimation in a resource constrained, neural network-based speech enhancement task is investigated. The first DoA estimator is a Non-negative Matrix Factorization weighted Steered Response Power beamformer abbreviated as the SRP-NMF. The broadband SRP beamformers cannot perform multi-speaker DoA estimation in a single time frame, a drawback which is overcome in the SRP-NMF by NMF weighting. The weights are obtained by NMF of the mixture spectrogram and correspond to the NMF atoms of the underlying sources. On evaluations conducted on data from public challenges and data generated from recorded room impulse responses and with various microphone array configurations, the SRP-NMF method outperforms the widely used variants of narrowband and broadband DoA estimators in terms of source detection capa-bility and DoA estimation accuracy. The second DoA estimator, SFF-PHAT-env, estimates the directional information of the sources by, PHAse Transform (PHAT) weighted cross-correlation of the amplitude envelopes at several frequencies (obtained by passing the microphone signals through a narrowband filter called Single Frequency Filtering (SFF)) across the channels. The high signal-to-noise ratio regions in the envelopes, PHAT weighting, and multiple evidences at several frequencies, result in robust DoA estimates. The performance of SFF-PHAT-env is compared with the other existing SFF-based methods and the state-of-the-art Generalized Cross Correlation (GCC)-based methods. The tests are conducted on publicly available data collected in real rooms in challenging conditions From the experiments, it is observed that the best performing SFF-based methods are better or comparable to the best GCC-based estimator in detection metrics such as F-measure and accuracy metrics based on azimuth error. Irrespective of whether the TF mask is obtained as an ideal binary mask (IBM) or by any practical method, there are often regions of the target speech that are suppressed, leading to audible artefacts. The influence of these errors could be reduced by estimating such missing data points by some form of interpolation using NMF. The existing NMF-based methods of interpolation are computationally intensive and do not offer a means to control the degree of interpolation, resulting in over-estimation of the missing regions and leading to noise-vocoded output. In the proposed NMF-based interpolation method, we address the drawbacks of the existing methods by considering the improvement achievable by applying the proposed method to ideal binary mask-based gain functions. The instrumental quality metrics (PESQ and SNR)) indicate the added benefit of the missing data interpolation compared to the output of the ideal binary mask. In resource constraint devices, the learning model cannot be complex. Hence the demand to perform a task is on the input features. The more discriminative the features, the better is the performance. Eigenvalues are spectral features that can discriminate coherent sound sources from the spatially uncorrelated ones. However, the eigenvalues have not been used for neural-network-based enhancement. In this thesis, for extracting speech from noise, we explore the efficacy of the instantaneous generalized eigenvalue f

Abstract

Data mining is a process of extracting interesting information from the large volumes of data. Data mining algorithms are being widely used in several applications like customer relationship management, inventory management, recommendation systems, fraud detection, and surveillance. Pattern mining is an important task of data mining, which involves discovering interesting associations from large trans- actional databases such as market basket data, e-commerce transactions, social network transactions, etc. In the literature, efforts are being made to investigate different kinds of pattern mining models such as frequent patterns, periodic patterns, utility patterns, coverage patterns, and correlated patterns. Also, research efforts are being made to propose MapReduce based approaches to improve the scalability of pattern mining approaches. In this thesis, we investigate improved approaches to extract partial periodic patterns (3Ps) from a given spatio-temporal database. In the literature, the model of periodic patterns has been proposed to extract the interesting associations, which appear periodically in a large temporal database. Moreover, a model of 3Ps has also been proposed to extract the knowledge of interesting associations, which appear periodically in a partial manner in a large temporal database. However, the issue of extracting 3Ps from spatio-temporal databases has not addressed. In this thesis, we have proposed an improved model and algorithms to extract 3Ps from spatio-temporal databases. Moreover, we have also proposed MapReduce framework to extract 3Ps from large spatio-temporal database. A 3P in the given temporal database is defined based on the user-given minimum periodic-support and maximum inter-arrival time constraints. For extracting 3Ps from a given temporal database, Pattern- growth and ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal) based approaches have been proposed in the literature. It has been observed that the existing model of extracting 3Ps (and the algorithms) face performance issues for extracting 3Ps in a spatio-temporal database scenario. Based on the observation that the itemsets or patterns having large spatial distance between the items may not be interesting, we have identified the notion of maximum distance as the additional pruning criteria and proposed the improved model of 3Ps. Overall, the proposed model employs minimum periodic-support, maximum inter-arrival time, and maximum distance as three constraints to determine the interestingness of a pattern in a spatio-temporal database. The minimum periodic-support is equal to the minimum number of periodic occurrences of a pattern within the data. The maximum inter-arrival time is equal to the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The maximum distance is equal to the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned as interesting patterns. Based on the new model, we have proposed both ECLAT and pattern growth based algorithms to extract 3Ps from the given spatio-temporal databases. The efficiency of our approaches is shown by conducting experiments on large synthetic and real-world spatio-temporal databases. We also demonstrate the usefulness of the extracted patterns through a case study by applying on air pollution and congestion datasets. To improve scalability, we have also proposed a parallel MapReduce based pattern growth approach to discover 3Ps in a spatio-temporal database. The issue is to maintain the load balancing among the ma- chines and mine 3Ps in parallel by employing a large number of machines. To address the issue, we have proposed a parallel algorithm by incorporating the step of distributing transactional identifiers among the machines and mining the identical itemsets independently over the different machines. We have proposed an improved load allocation algorithm such that the machines receive the balanced load, ap- proximately. Experimental results conducted on Apache Spark’s distributed environment show that the proposed parallel approach improves performance significantly with increase in number of machines.

Abstract

The current generation of quantum computers is widely referred to as noisy intermediate-scale quantum (NISQ) computers. These devices are predecessors of fault-tolerant quantum computers, which are speculated to perform calculations that will be intractable for even the most powerful classical supercomputers. However, unlike fault-tolerant hardware, the computational capabilities of the near-term hardware are primarily affected by their limited qubit count, restrictive hardware topology, and lack of error correction. A natural question arises whether there are any practical applications for these NISQ devices in performing calculations of independent interest. This dissertation focuses on employing NISQ hardware for solving computational problems more efficiently. We begin by posing this problem into a series of key research questions that are built on understanding the noises present in these devices and designing software to tailor algorithms to be more noise resilient. Our major contributions are then based on answering each of these questions individually. In the beginning, we give a didactic introduction to quantum computation and NISQ architecture along with the self-contained theory for hybrid quantum-classical algorithms. We also provide a comprehensive review of the potential areas for applications of these hybrid algorithms and the field of quantum error mitigation. We then present Aakash, a software simulator based on IBM Qiskit, that models a noisy quantum processor based on density matrix formalism. This simulator is currently being used as a backend for the QSim: Quantum Computer Simulator Toolkit, one of India’s first initiatives under the Quantum-Enabled Science & Technology (QuEST) program to address the common challenge of advancing the quantum computing research frontiers in India. Furthermore, we showcase formulations for applications related to combinatorial optimization and quantum chemistry that can be used to solve problems using variational quantum algorithms such as quantum approximate optimization algorithm (QAOA) and variational quantum eigensolver (VQE), respectively. We also perform noise analysis for these algorithms, characterizing how different types of noise-based errors affect their performance. Our simulation of QAOA provides insightful data about the effect of errors due to amplitude decay, decoherence, thermalization, and logic gate imprecision, which will help in designing specific quantum hardware for variational quantum algorithms that will allow us to approximately solve NP problems more efficiently. We further use a VQE-based method to calculate molecular energy derivatives on a quantum computer for both ground state energy and excited state energies up to the second order. Other than minimum energy configuration search for H2 molecule, estimation of molecular response properties such as dipolemoment and polarizability, we also pursue transition state search for the reaction H2+H↔H+H2. Our variational method gives results in complete agreement with those obtained using full configuration interaction (FCI) values. We also present qLEET, an open-source library for exploring loss landscape, expressibility, entangling capability, and training trajectories of noisy parameterized quantum circuits. It supports quantum circuits and noise models built using popular quantum computing libraries such as Qiskit, Cirq and Pyquil while providing opportunities to design new hybrid algorithms by utilizing intuitive insights from the ansatz capability and structure of the loss landscape. Finally, we show how NISQ hardware can be designed and utilized in a more circuit-centric fashion by studying the dependence of the circuit size, circuit depth on the interaction and connection between different qubits present in quantum hardware. Throughout our work, we have not performed any quantum computations beyond the reach of the current capabilities of classical computers. Still, we do address many implementation challenges that must be overcome in such an endeavor, including circuit optimization, efficient compilation, and error mitigation. We conclude by providing an optimistic outlook towards the incremental utilization of quantum computers and explicitly stating how our work could help with it

Abstract

Extracting knowledge from large amounts of data has been an important research field for the past three decades. Data mining is one such area that deals with the investigation of frameworks and algorithms to extract different kinds of knowledge from large databases. Pattern mining is one of the data mining tasks, which deals with developing frameworks and algorithms for extracting interesting patterns (or itemsets) from large databases. In the literature, several pattern mining techniques have been employed to mine different kinds of patterns such as frequent patterns, coverage patterns, periodic patterns, weighted patterns, utility patterns, etc. In this thesis, we investigate improved approaches for mining weighted and high utility itemsets. As a first contribution, we have proposed an improved approach to mine Weighted Frequent Itemsets (WFIs) from spatio-temporal databases. The WFI framework allows the users to set weights to the items present in the database based on their importance and discovers all itemsets whose weighted sum in a transactional database is no less than the user-specified minimum weighted sum (minW S) value. The existing works in the literature have focused on finding WFIs in a transactional database and did not consider the spatial characteristics of the items within the data. The spatial characteristics of an item imply the geographical location of an item. We have proposed an improved WFI Mining (WFIM) approach for spatio-temporal databases by introducing the notion of neighborhood itemset, which is defined by considering the spatial characteristics of the items. An itemset is called a neighborhood itemset if the distance between each pair of items is no more than the user-specified maximum distance (maxDist) value. By adding the notion of neighborhood itemset to WFIM, we have proposed a new model, which we call Weighted Frequent Neighborhood Itemset Mining (WFNIM), to mine Weighted Frequent Neighborhood Itemsets (WFNIs) from spatiotemporal data. The generated WFNIs do not satisfy the anti-monotonic property. To extract WFNIs efficiently, we have proposed new pruning measures by exploiting the spatial nature of items to effectively reduce the search space and the computational cost of finding the desired patterns. A new pattern-growth algorithm has been presented to find all WFNIs from a given spatio-temporal database. Experimental results demonstrate that the proposed algorithm is efficient. A case study on JAPAN air pollution data is presented to demonstrate the usefulness of this model. As a second contribution, we have proposed an improved High Utility Itemset Mining (HUIM) approach. HUIM uses utility measure to mine High Utility Itemsets (HUIs) from the transactional database. In HUIM, each item is associated with internal utility (frequency of an item within the transaction) and external utility (cost of the item). The utility of an item is defined as the product of external and internal utilities. An itemset is called a high utility itemset if its utility is no less than the userspecified minimum utility (minU til) value. It can be noted that HUIM suffers from low utility item problem. To overcome this problem, we introduce a new null-invariant measure, called utility ratio, to evaluate the interestingness of an itemset in the database. We have extended HUIM to propose a new model of Relative High Utility Itemset Mining (RHUIM) to mine Relative High Utility Itemsets (RHUIs) in transactional data. An itemset is called RHUI if its utility is no less than the minU til value and its utility ratio is no less than the user-specified minimum utility ratio (minUR) value. We have proposed two new pruning measures based on utility ratio to reduce the search space. We also present a fast algorithm to find all desired itemsets in a transactional database. Experimental results demonstrate that the proposed algorithm is efficient. A case study on the real-world Yahoo! JAPAN retail data is presented to demonstrate the usefulness of this model. Overall, we have proposed improved approaches to discover weighted frequent neighborhood itemsets from spatio-temporal databases and relative high utility itemsets from transactional databases. We have demonstrated efficiency by conducting extensive experiments on real-world and synthetic databases. We have also demonstrated the application of the proposed approaches by applying the proposed approaches in real-world scenarios.

Abstract

In this thesis, we address the age old problem of dealing with scenarios in which the significance of making a mistake while taking a classification decision is very high. We also look deeper into how we can take advantage of deep learning techniques to establish state of the art results and study the representations learnt by the deep reject option classifiers. While supervised techniques have their advantage we explored unsupervised learning in real time scenario of catching malicious traders in Stock Exchanges which are establishments where significance of mistakes are very high. Firstly, we propose deep architectures for learning binary instance specific abstain (reject option) classifiers. To show the effectiveness of the proposed approach, we experiment with several real world datasets. We observe that the proposed approach not only performs comparable to the state-of-the-art approaches, it is also robust against label noise. We also provide visualizations to observe the important features learned by the network corresponding to the abstaining decision. Secondly, we look at Stock exchanges. Due to their prominence, stock exchanges are prone to a variety of attacks. Different types of proprietary fraudulent activity detectors are deployed by stock exchanges to analyze the time series data of trader’s activities or the activity of a particular stock to flag potentially malicious transactions while human analysts probe the flagged transactions further. The key issue faced here is that while the number of anomalous transactions identified can run into thousands or tens of thousands, the number of such transactions that can realistically be probed by human analysts would be a small fraction due to resource constraints. The issue therefore reduces to a dynamic resource allocation problem wherein alerts that represent the most malicious transactions need to be mapped to human analysts for further probing across different time intervals. Thirdly, we develop a multiclass deep learning solution with a rejection option. While binary so- lutions find relevance in scenarios such as cancer detection where ”yes” or ”no” could be the probable options. In scenarios such as autonomous driving, multiple options could be applicable at each instant. So, we propose a multiclass double ramp loss function. We study the properties of the loss function and establish state of the art results on real world datasets.

Abstract

Binocular vision (BV) is the result of the fusion of the input from each eye to form a coherent image. BV is essential for an individual to have a normal vision. Clinically BV anomalies (strabismus, amblyopia) are evaluated using different diagnostic tests and instruments. In recent studies, non-strabismus BV anomalies have also been reported and flagged to be included in conventional screening protocols. A traditional instrument is the Synoptophore which evaluates three grades of BV. This equipment is very efficient but has certain limitations a) ambient light in the testing room, b) bulky and hence difficult to transport to screening camps in remote areas, c) Cost of Synoptophore is another major disadvantage, making it out of reach of most eye care centers etc. The Aim of this study is to address these limitations with existing technology and provide a suitable solution for the same. Virtual Reality(VR) is one such rapidly evolving technology which provide a suitable platform to implement the principle of Synoptophore easily. Therefore, we propose VR-Phore, a VR application solely based on principle of the haploscope as in the Synoptophore. Which uses a head-mounted display device (Oculus Rift is used for this study) for diagnostics and also as possible therapeutic use. The proposed system addresses the limitations of the Synoptophore. Limitations such as requirement of suiatable light source in the room while using synoptophore, there is no such requirement for VR-Phore usage. VR-Phore is a simple VR application which can be easily set up at any places like screening camps in schools, clinics or in any remote areas etc. Operating Synoptophore requires a clinical professional with prior knowledge on the device whereas VR-Phore can be used by anyone and the stored data can be provided to doctors for diagnosis. VR-Phore also provide added advantages of a software platform to incorporate testing modules for a range of clinical conditions very easily and tele-diagnosis. A total of 41 participants are involved in this study for data collection. The data collected from this system was compared to Synoptophore. Measurements, high correlations and accuracy was observed validating the application of the proposed system in diagnostics. The added advantage is the portability of the software platform onto a smart phone attached to a low-cost VR head-mounted device, effectively making this system cost-friendly and affordable to everyone.

Abstract

Online abuse and offensive language on social media have become widespread problems in today’s digital age. With the growing use of the web in our daily lives, identifying and countering such speech has become a popular topic in the research community. Authorities of many countries worldwide recognise this as a serious problem, particularly because the reach of the internet has grown exponentially over the past years. Most previous work in this area has focused on detecting such harmful speech to prevent users from experiencing the adverse impact of the same. Detection is achieved chiefly through text classification models, which learn to recognise hate speech/offensive speech from neutral/standard sentences. More recently, with the advent of style transfer in text, many such negative attributes of speech are transferred into positive ones. One such instance is sentiment transfer, in which negative reviews are rewritten as positive ones while attempting to preserve the non-stylistic content of the input. In this thesis, we contribute the JDC (Jibe and Delight Corpus), a Reddit-based dataset consisting of 68, 159 insults and 51, 102 compliments. Our dataset is unique and differs from typical online abuse datasets since instead of being targeted at a community or race (in case of hate speech), they are targeted at individuals, and exhibit “creativity” in their formation, and so do not rely on profanity or slurs to convey the negative intent of the insult. We also perform extensive experiments on the same on two different fronts. We first work on the detection of such speech by analysing the results of various types of text classification models. Secondly, we propose to convert such insults into compliments by using unsupervised style transfer. We benchmark multiple existing state-of-the-art models for both text classification and unsupervised style transfer on the dataset. Our best classification model shows an accuracy of 97.8% and an F1 score of 0.972. We analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.

Abstract

With extensive use of the Internet in recent years, massive amounts of data related to different topics and developments have been generated and shared online. The advent of social media like Twitter, Facebook, and Reddit has accelerated the communication between people of all colors, nations, and languages. Though the platform exists, communication barriers still exist due to languages. Many researchers are trying to solve this through various methods. India is a land of multiple languages, and a majority of people are multilingual and tend to mix words from different languages in written text and also in speech. India has twenty-three significant languages with over seven hundred and twenty dialects. Kannada is a Dravidian language, and it is one of the four main Dravidian languages, and it is one of the top 30 most spoken languages of the world, with its own independent script and over fifty million speakers. With recent surges in developments in machine learning, understanding the language, and thus resulting in a wide variety of applications for resource-rich languages. In resource-rich languages, a considerable amount of work has been performed. The same is not true for low-resource languages or even Code-mixed low-resource languages. Kannada is one of such low-resource languages. In this thesis, we aim to focus on the research areas related to the Kannada-English Code-mixed domain. We present corpora in Kannada-English Code-mixed Twitter data and their analysis in the areas of Emotion Prediction and Parts-of-Speech Tagging, respectively. In the first part, we present the corpus for Emotion Prediction in Kannada-English Code-mixed data. This is, to the best of our knowledge, the first corpus in this field. Identification and analysis of emotions in user-generated data in social media like Twitter, Facebook, Reddit, etc., is essential in understanding the daily trends and human behavior. There has been work done on Code-mixed social media corpus but not on emotion prediction of Kannada-English Code-mixed Twitter data. We analyze the problem of emotion prediction on corpus obtained from Code-mixed KannadaEnglish extracted from Twitter annotated with their respective ‘Emotion’ for each tweet. We experimented with machine learning prediction models using features like Character N-Grams, Word NGrams, Repetitive characters, and others on Support Vector Machines(SVM) and Long Short term Memory Network(LSTM) on our corpus. In the second part, we present the corpus in Kannada-English Code-mixed data for Parts-of-Speech Tagging. This is, to the best of our knowledge, the first corpus in this field. Part-of-Speech (POS) is essential for many Natural Language Processing (NLP) applications. There has been a significant amount of work done in POS tagging for resource-rich languages. POS tagging is an essential phase of text analysis in understanding the semantics and context of language. These tags are useful for higher-level tasks such as building parse trees, which can be used for Named Entity Recognition, Coreference resolution, Sentiment Analysis, and Question Answering. There has been work done on Code-mixed social media corpus but not on POS tagging of KannadaEnglish Code-mixed data. Here, we present Kannada-English Code-mixed social media corpus annotated with corresponding POS tags. We also experimented with machine learning classification models Conditional Random Fields(CRF), Bidirectional Long Short term Memory Network(Bi-LSTM), and Bidirectional Long Short term Memory Network-Conditional Random Fields(Bi-LSTM-CRF) models on our corpus.

Abstract

Energy consumption of the residential sector is increasing year after year with economic development, urbanization, and improvement of people’s living standards. In order to achieve more efficient energy consumption, it is crucial to provide appliance-wise energy consumption feedback to the users to save energy. Appliance wise energy consumption feedback allows users to identify faulty appliances, reduce energy demand and identify unnecessary active devices. Individual appliance energy consumption is monitored by two approaches intrusive load monitoring and non-intrusive load monitoring. Intrusive load monitoring uses smart plugs and smart strips on each device to monitor their power consumption.Non-intrusive load monitoring (NILM) estimates the individual appliance power consumption from the main meter aggregated data without using additional sensors. NILM is a cost-effective approach for giving appliance-wise energy consumption feedback. Non-intrusive load monitoring (NILM) is a blind source separation problem that requires a system to estimate the electricity usage of individual appliances from the aggregated household energy consumption. In this thesis, we will discuss about NILMmethods and algorithms on low sampled (1 min) aggregated data in detail and identify the research gaps. To addresses those research gaps, we propose a novel deep neural network-based approach for performing load disaggregation on low-frequency power data (1 min) obtained from residential households. We combine a series of one-dimensional Convolutional Neural Networks and Long Short Term Memory(1D CNN-LSTM) to extract features that can identify active appliances and retrieve their power consumption given the aggregated household power value. We took appliances which are multi-state appliances(washing machine, dishwasher, microwave, and refrigerator ) to test the algorithm. The disaggregation performance of the algorithm is measured using six metrics and compared with five stateof-the-art algorithms. To explore how well the algorithms generalize to unseen houses, the performance of the algorithms was measured in two separate scenarios: one using test data from a house not seen during training and a second scenario using test data from houses that were seen during training. Our neural net achieves better F1 scores (across all four appliances) than state-of-the-art algorithms and generalizes well to unseen houses with a lower number of trainable parameters. The algorithm is designed for low-power offline devices. Empirical calculations show that our model outperforms the state-of-theart on Reference Energy Disaggregation Dataset (REDD) and UK-dale dataset. We have collected the electricity consumption data of 11 houses in India for 19 days as part of the Residential Building Energy Demand in India (RESIDE) Project. The proposed model on RESIDE dataset achieved better F1 scores and generalised well on the unseen houses.

Abstract

In communication systems, the reliable transmission of message bits is an important factor in the overall efficiency of the system. The correction of errors encountered in transmitted messages is done with the use of error correcting codes. These codes have traditionally been designed with concepts of abstract algebra. In the coding theory domain, the pursuit of good decoders and channel code designs has always been an interesting area of research. Recently because of the use of advanced GPU machines, neural networks have become a tool of choice for modern day researchers to do various tasks like function approximations and algorithm learning. There has been a surge in the research work trying to develop end to end codes and decoders for channel coding applications. Explorations into the modulation and demodulation designs for various channel settings were the first to come up in the applications of neural networks for channel coding. Following this came up the design of decoders for various kinds of existing codes in the coding theory. Then, explorations into the design of end to end communication systems and autoencoders came up in the literature. We have come up with a design of a decoder for a coded modulation technique called as TCM (trellis coded modulation). The design is based on RNN and CNN variants of neural networks and shows that neural networks can perform the task of decoding coded modulation messages as effectively as the existing algorithms for codeword decoding. The trained model is tested for robustness to channel models not seen while training, and the proposed decoder is better in performance as compared to the existing decoder. An interpretation of the working of the proposed decoder has also been presented in this thesis, where we show the classification of symbols into decision regions with the use of CNN along with softmax activation function. In another work, we have implemented an autoencoder based on neural networks for product code topology. The autoencoder takes ideas from turbo product codes for its design. The idea behind its operation is that the code is divided into its component row and column codes, the operation of the encoding is done by applying row and column codes one after another to generate the codeword. The decoder utilises the extrinsic information extraction equations to do the decoding of the received codeword. The received codeword is decoded in a row wise and column wise manner to get confidence values for every bit in the transmitted codeword; these are then used in the information extraction equations. The training of the autoencoder is done for the AWGN channel and it shows performance improvement over uncoded BPSK modulated messages. The encoder performance can be improved with the use of higher codeword lengths while training. With the use of individually trained row and column component codes in the autoencoder, the performance of the proposed autoencoder may get further improved.