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It consists of C/C++ and Matlab-code. openSMILE provides a full set of functions which cover the entire speech and music processing pipeline: * Audio processing: Recording, feature extraction, feature analysis, signal processing, power spectrum analysis. * Speech recognition: Mel-frequency cepstral coefficients (MFCC) based speech recognition using the FASTA system * Music analysis: Time-frequency analysis of audio, including feature extraction, MFCC-based, spectral envelope (SE) and zero-crossing rate (ZCR) analysis. * Paralinguistics recognition: Paralinguistic recognition including fundamental frequency (F0), pitch tracking and speaker verification. openSMILE uses several audio/music features, some of them are listed below. The following parameters must be adjusted by the user: * The number of mel-bands to use for each channel. * The number of frames to use for each window. * The number of bins to use for the FFT analysis of each frame. * The sampling rate of the recorded signal. * The sampling rate of the extracted features. * The number of training samples to use for training. The following parameters are automatically determined by openSMILE. * The number of channels. * The time-frequency resolution used by openSMILE. * The number of mel-bins in the MFCC feature. * The number of FFT-bins. * The number of frames in the FFT analysis. openSMILE computes an audio power spectrum analyzer. The main window of openSMILE consists of two tabs: * The audio source is connected to the microphone. * The analysis window can be connected to the speaker. If a connection is made, the audio source is triggered. If a connection is made, openSMILE will analyze all channels and all windows. openSMILE will run in real time, What's New in the? Speech & Music Interpretation by Large Space Extraction (SMILE) is a Java application for feature extraction and analyses of audio signals. It provides a framework which can be used as a powerful tool for a wide range of applications, such as automatic speech/music recognition, language identification and speaker recognition. For speech/music processing, the audio signal is filtered into different frequency bands. Those frequency bands are then calculated with a set of time-frequency transforms. The output of these processes can be used for feature extraction. Those features are then fed into an application specific classifier, such as a Gaussian Mixture Model (GMM) or a hidden Markov model (HMM). Requirements: OpenSMILE supports a variety of platforms, including Windows, Unix, and Mac OS X. OpenSMILE uses the following software: GNU Octave (2.4.0 or newer) - for data preprocessing GNU C++ (g++ version 4.1.2 or newer) - for feature extraction GNU LibSVM (3.0.1 or newer) - for feature selection and classification GNU Java runtime environment (1.4.2 or newer) - for classification OpenSMILE is able to run on PCs with 256 MB or more RAM and on standard desktop computers. OpenSMILE also includes a library which enables OpenSMILE to be used in embedded environments, such as in gateways, PCs with limited resources, PDAs and set-top boxes. Applications and Research OpenSMILE can be used in a variety of applications, ranging from research, to the detection of domains such as language, or musical genre and style. It supports a range of features including: Evaluation of the features extracted from the audio data can be carried out using a range of well-established evaluation algorithms. For speech applications, OpenSMILE can incorporate the AMI evaluation measures. SMILE also provides software to allow it to be used in other research areas. These include the use of audio as input to be used in conjunction with other data sources. OpenSMILE can be used to extract features from images in conjunction with spoken data. the use of its tools and features to be used as input to be used as features in an alternative method for sentiment analysis. This can be applied to the generation of music and audio for emotional communication. References External links OpenSMILE web site Category:Audio software for Linux Category:Feature extraction (computer vision) Category:Free software programmed in Java (programming language) Category:Free speech recognition software Category:Speech recognition software Category:Text-based computer speech recognition Category:Free audio software Category:Free music software Category:Free music software Category:Free music software Category System Requirements: Please see below a list of requirements: Minimum: OS: Windows 7 SP1/ Windows 10 Windows 7 SP1/ Windows 10 CPU: Intel Core i3 Intel Core i3 Memory: 4 GB RAM 4 GB RAM Graphics: NVIDIA GeForce GTX 660 (2 GB VRAM) NVIDIA GeForce GTX 660 (2 GB VRAM) HDD: 25 GB available space 25 GB available space Sound Card: onboard Recommended: Windows
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