Instantaneous pitch estimation algorithm based on multirate sampling

The paper presents an algorithm for accurate pitch estimation that takes advantage of the sinusoidal model with instantaneous parameters. The algorithm decomposes the signal into subband components, extracts their instantaneous parameters and evaluates period candidate generating function (PCGF). In order to achieve high accuracy for low and high-pitched sounds it is assumed that possible pitch variation range is proportional to current pitch value. The bandwidths of the decomposition filters and length of the analysis frame are scaled for each period candidate by multirate sampling. The algorithm is compared to other widely used pitch extractors on artificial quasiperiodic signals and natural speech. The proposed algorithm shows a remarkable frequency and time resolution for pitch-modulated sounds and performs well both in clean and noisy conditions

A modulation property of time-frequency derivatives of filtered phase and its application to aperiodicity and fo estimation

We introduce a simple and linear SNR (strictly speaking, periodic to random power ratio) estimator (0dB to 80dB without additional calibration/linearization) for providing reliable descriptions of aperiodicity in speech corpus. The main idea of this method is to estimate the background random noise level without directly extracting the background noise. The proposed method is applicable to a wide variety of time windowing functions with very low sidelobe levels. The estimate combines the frequency derivative and the time-frequency derivative of the mapping from filter center frequency to the output instantaneous frequency. This procedure can replace the periodicity detection and aperiodicity estimation subsystems of recently introduced open source vocoder, YANG vocoder. Source code of MATLAB implementation of this method will also be open sourced.Comment: 8 pages 9 figures, Submitted and accepted in Interspeech201

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

ОБРАБОТКА РЕЧЕВЫХ СИГНАЛОВ В ПРИЛОЖЕНИЯХ МУЛЬТИМЕДИА НА ОСНОВЕ ПЕРИОДИЧЕСКОЙ МОДЕЛИ С НЕСТАЦИОНАРНЫМИ ПАРАМЕТРАМИ

Methods of time-varying speech parameterization for analysis, processing and synthesis in multimedia systems. The main theoretical points are given and practical issues are discussed. Some practical results of instantaneous pitch estimation and quality of voice morphing are presented.Рассматриваются методы нестационарной параметризации речевых сигналов, позволяющие выполнять анализ, обработку и синтез речи в приложениях мультимедиа. Формулируются основные теоретические положения и рассматриваются вопросы практической реализации. Приводятся результаты применения методов к задачам оценки основного тона и изменения просодических характеристик речевого сигнала