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    Parallel implementation of deep neural networks for classifying heart sound signals
    11.
    发明授权
    Parallel implementation of deep neural networks for classifying heart sound signals 有权

    公开(公告)号：US11432753B2

    公开(公告)日：2022-09-06

    申请号：US16534955

    申请日：2019-08-07

    Applicant: Tata Consultancy Services Limited

    Inventor： Shahnawaz Alam ,  Rohan Banerjee ,  Soma Bandyopadhyay

    IPC: G06K9/62 ,  A61B5/316 ,  A61B5/00 ,  G06N3/04 ,  A61B5/349

    Abstract: Conventional systems and methods of classifying heart signals include segmenting them which can fail due to the presence of noise, artifacts and other sounds including third heart sound ‘S3’, fourth heart sound ‘S4’, and murmur. Heart sounds are inherently prone to interfering noise (ambient, speech, etc.) and motion artifact, which can overlap time location and frequency spectra of murmur in heart sound. Embodiments of the present disclosure provide parallel implementation of Deep Neural Networks (DNN) for classifying heart sound signals (HSS) wherein spatial (presence of different frequencies component) filters from Spectrogram feature(s) of the HSS are learnt by a first DNN while time-varying component of the signals from MFCC features of the HSS are learnt by a second DNN for classifying the heart sound signal as one of normal sound signal or murmur sound signal.

    Identification of People Using Multiple Skeleton Recording Devices
    15.
    发明申请
    Identification of People Using Multiple Skeleton Recording Devices 审中-公开
    使用多个骨架记录装置识别人

    公开(公告)号：US20140341439A1

    公开(公告)日：2014-11-20

    申请号：US14280353

    申请日：2014-05-16

    Applicant: TATA CONSULTANCY SERVICES LIMITED

    Inventor： Kingshuk CHAKRAVARTY ,  Aniruddha SINHA ,  Diptesh DAS ,  Rohan Banerjee ,  Amit Konar ,  Sudeepto Dutta

    IPC: G06K9/00 ,  G06K9/62

    CPC classification number: G06K9/00348 ,  G06K9/44 ,  G06K9/627

    Abstract: Method(s) and system(s) for identification of an unknown person are disclosed. The method includes receiving skeleton data comprises data of multiple skeleton joints of the unknown person from skeleton recording devices. The method further includes extracting G gait feature vectors from the skeleton data. Further, the method includes classifying each gait feature vector into one of N classes based on a training dataset for N known persons and computing a classification score for each class. The method also includes clustering the training dataset into M clusters based on M predefined characteristic attributes of the known persons, tagging each gait feature vector with one of the M clusters based on a distance between a respective gait feature vector and cluster centers of M clusters, and determining a clustering score for each M cluster. The method further includes identifying the unknown person based on clustering scores and classification scores.

    Abstract translation: 公开了用于识别未知人的方法和系统。 该方法包括从骨架记录装置接收包括未知人的多个骨骼关节的数据的骨架数据。 该方法还包括从骨架数据中提取G步态特征向量。 此外，该方法包括基于N个已知人员的训练数据集将每个步态特征向量分类为N类中的一个，并计算每个类的分类分数。 该方法还包括基于已知人员的M个预定义特征属性将训练数据集聚类成M个群集，基于各个步态特征向量与M个群集的簇中心之间的距离来标记每个步态特征向量与M个群集中的一个， 以及确定每个M簇的聚类分数。 该方法还包括基于聚类分数和分类分数识别未知人。

    Non-invasive detection of coronary heart disease from short single-lead ECG
    16.
    发明授权
    Non-invasive detection of coronary heart disease from short single-lead ECG 有权

    公开(公告)号：US11051741B2

    公开(公告)日：2021-07-06

    申请号：US16557904

    申请日：2019-08-30

    Applicant: Tata Consultancy Services Limited

    Inventor： Rohan Banerjee ,  Sakyajit Bhattacharya ,  Soma Bandyopadhyay ,  Aniruddha Sinha

    IPC: A61B5/025 ,  A61B5/316 ,  A61B5/024 ,  A61B5/25 ,  A61B5/00 ,  A61B5/021

    Abstract: Electrocardiography (ECG) signals contain important markers for Coronary Heart Disease (CHD). State of the art systems and methods rely on clinically available multi-lead ECG for CHD classification which is not cost effective. Moreover the state of the art methods are applied on digital ECG time series data only. Also, discriminative HRV markers are not often present in short ECG recordings necessitating long hours of ECG data to analyze. In accordance with the present disclosure, systems and methods described hereinafter extract ECG time series from ECG images obtained from commercially available low-cost single lead ECG devices through a combination of image and signal processing steps including Histogram analysis, Morphological operation-thinning, Extraction of lines, Extraction of Reference Pulse, Extraction of ECG and interpolating missing data. Further, domain independent statistical features such as self-similarity of raw ECG time series and average Maharaj's distance along with domain specific features are used for classifying CHD.

    Real-time traffic detection
    20.
    发明授权
    Real-time traffic detection 有权
    实时流量检测

    公开(公告)号：US09424743B2

    公开(公告)日：2016-08-23

    申请号：US14431053

    申请日：2013-10-10

    Applicant: TATA CONSULTANCY SERVICES LIMITED

    Inventor： Rohan Banerjee ,  Aniruddha Sinha

    IPC: G08G1/01 ,  G08G1/04

    CPC classification number: G08G1/01 ,  G08G1/0104 ,  G08G1/0133 ,  G08G1/04

    Abstract: Systems and methods for real-time traffic detection are described. In one embodiment, the method comprises capturing ambient sounds as an audio sample in a user device, and segmenting the audio sample into a plurality of audio frames. Further, the method comprises identifying periodic frames amongst the plurality of audio frames. Spectral features of the identified periodic frames are extracted, and horn sounds are identified based on the spectral features. The identified horn sounds are then used for real-time traffic detection.

    Abstract translation: 描述用于实时流量检测的系统和方法。 在一个实施例中，该方法包括在用户设备中捕获环境声音作为音频样本，并将音频样本分割成多个音频帧。 此外，该方法包括识别多个音频帧之间的周期性帧。 提取识别的周期帧的光谱特征，并且基于光谱特征识别喇叭声音。 然后，识别的喇叭声用于实时流量检测。

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