Dendritic Computing: Branching Deeper into Machine Learning

Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.

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Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering

The state-of-the-art deep learning-based object recognition YOLO algorithm and object tracking DeepSORT algorithm are combined to analyze digital images from fluid dynamic simulations of multi-core emulsions and soft flowing crystals and to track moving droplets within these complex flows. The YOLO network was trained to recognize the droplets with synthetically prepared data, thereby bypassing the labor-intensive data acquisition process. In both applications, the trained YOLO + DeepSORT procedure performs with high accuracy on the real data from the fluid simulations, with low error levels in the inferred trajectories of the droplets and independently computed ground truth. Moreover, using commonly used desktop GPUs, the developed application is capable of analyzing data at speeds that exceed the typical image acquisition rates of digital cameras (30 fps), opening the interesting prospect of realizing a low-cost and practical tool to study systems with many moving objects, mostly but not exclusively, biological ones. Besides its practical applications, the procedure presented here marks the first step towards the automatic extraction of effective equations of motion of many-body soft flowing systems.

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Tracking droplets in soft granular flows with deep learning techniques.

We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets' shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled micro organisms in complex biological flows.

A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions

This article presents an overview of the POLYCOST database dedicated to speaker recognition applications over the telephone network. The main characteristics of this database are: large mixed speec ...

Polycost : A telephone-speech database for speaker recognition

Voice comparison is a variant of speaker recognition or voice recognition. Voice comparison plays a significant role in the forensic science field and security systems. Precise voice comparison is a challenging problem. Traditionally, different classification and comparison models were used by the researchers to solve the speaker recognition and the voice comparison, respectively but deep learning is gaining popularity because of its strength in accuracy when trained with large amounts of data. This paper focuses on an elaborated literature survey on both traditional and deep learning-based methods of speaker recognition and voice comparison. This paper also discusses publicly available datasets that are used for speaker recognition and voice comparison by researchers. This concise paper would provide substantial input to beginners and researchers for understanding the domain of voice recognition and voice comparison.

Nishtha H. Tandel

Papers

Voice Recognition and Voice Comparison using Machine Learning Techniques: A Survey