vasukumar92/Trigger-Word-Detection-for-a-Laptop-using-Deep-Learning
In this notebook, we will construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword de...

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In this notebook, we will construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word. For this exercise, our trigger word will be "Activate." Every time it hears you say "activate," it will make a "chiming" sound. By the end of this assignment, you will be able to record a clip of yourself talking, and have the algorithm trigger a chime when it detects you saying "activate."

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This paper is continuously updated with deep anomaly detection methods and their applications

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Collection of papers and other resources for object tracking and detection using deep learning

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Wake-up-word(WUW)system is an emerging development in recent times. Voice interaction with systems have made life ease and aids in multi-tasking. Apple, Google, Microsoft, Amazon have developed a custom wake-word engine, which are addressed by words such as β€˜Hey Siri’. β€˜Ok Google’, β€˜Cortana’, β€˜Alexa’. Our project focuses initially only detection and response to a customized wake-up command. The wake-up command used is β€œGOLUMOLU”. A wake-up-word detection system search for specific word and reads the word, where it rejects all other words, phrases and sounds. WUW system needs only less memory space, low computational cost and high precision. Artificial Neural Networks(ANN) have reduced the complexity, computational time, latency, thus the efficiency of system has improved. Deep learning has improved the efficiency of automatic speech recognition(SR), where wake word detection is a subset of SR but unlike keyword spotting and voice recognition. A deep learning RNN model is used for the training of the network. RNN are specifically used in case of temporal sequence data and has the ability to process data of different length but of same dimension. For training a model, labelled dataset is needed. We generated three forms of data: golumolu, negative and background. Such that, the model learns circumspectly and attentively detects when specific word found. To start communication with system, the wake word should be delivered. The main task of WUW detection system is to detect the speech, to identify WUW words among spoken words, to check whether the word spoken in altering context.

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Collection of papers, datasets, code and other resources for object tracking and detection using deep learning

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Table Detection using Deep Learning

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Tensorflow, Luminoth Based Table Detection and Extraction

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Deep Learning Practices

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VGG-19 deep learning model trained using ISCX 2012 IDS Dataset

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Contains the programming exercises and assignments for Deep Learning A-Z course on Udemy. Includes Artificial Neural Network, Convolutional Neural Network, Recurrent Neural Networks, Self Organizing Maps, Auto Encoders and Boltzmann Machines

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Deep Learning Based Android Malware Detection Framework

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Autonomous driving involves perceiving and interpreting a vehicle’s environment using various sensors for controlling the vehicle, marking drivable areas and locating pedestrians. A pedestrian detector plays a key role demanding real time response. An efficient pedestrian detector must determine the exact location of a pedestrian in complex backgrounds, poses, illuminations, due to which it is a source of an active research for the last two decades. With the evolution of deep learning, there is no need of designing features which describe the pedestrian characteristics, instead the features can be learnt with the help of Convolutional Neural Networks (CNN). Our work includes training the YOLO v3 model on BDD100k dataset which is the largest and most diverse video dataset so far. It contains more pedestrian instances than previous specialized datasets, which makes it more viable for performing pedestrian detection. The results of training show that the proposed YOLO v3 network for pedestrian detection is well-suited for real-time applications due to its high detection rate and faster implementation. Idea By: Aditya Sharma, Microsoft

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Table Detection using Deep Learning FasterRCNN

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Deep learning based solution to automatically analyze medical images for malaria testing

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Unsupervised deep learning framework with online(MLP: prediction-based, 1 D Conv and VAE: reconstruction-based, Wavenet: prediction-based) settings for anaomaly detection in time series data

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