Results 151 to 160 of about 10,198 (208)

Adaptive teaching assistance model combining generative AI and big data analytics. [PDF]

Front Psychol
Zhu Z, Luo C, Li L.
europepmc   +1 more source

Stacked convolutional neural network for emotion recognition using multi feature speech analysis. [PDF]

Sci Rep
Roy C, Kharel W, Das P, Panigrahi R, Hareesha KS, Pramanik M, Bhoi AK.   +6 more
europepmc   +1 more source

FCAL-Net: a neural network model for speech emotion recognition of guanzhong dialect with multi-dimensional feature fusion. [PDF]

Sci Rep
Zhang L, Wang H, Li J, Zhou M, Guo X.
europepmc   +1 more source

AcousticSceneBD: A real-world environmental audio dataset for acoustic scene classification in Bangladesh. [PDF]

Data Brief
Mazumder TH, Ahmed A, Akter D, Ocin A, Ul Islam R, Rashid MRA, Ripon SH, Reza AW.   +7 more
europepmc   +1 more source

Measurement Method for Mold Slag Thickness in Continuous Casting Mold Using Millimeter-Wave Radar and Eddy Current Sensors. [PDF]

Sensors (Basel)
An Y, Wang Z, Xiao J.
europepmc   +1 more source

Balancing noise reduction and neural signature preservation in EEG biometrics. [PDF]

Sci Rep
Usman M, Sultan N, Nasim A, Ayaz B, Fatima J, Nosheen F.   +5 more
europepmc   +1 more source

Acoustic Features of Pain-Induced Vocalisations Are Associated with Subsequent Growth and Temperament Following Thermal Disbudding in Goat Kids: A Preliminary Study. [PDF]

Animals (Basel)
Fu Z, Gong Y, Hou W, Li L, Zhang H.
europepmc   +1 more source

Sibilant Representation Using MFCCs and GMMs

Sibilant Representation Using MFCCs and GMMs
openaire   +1 more source

Classification of speech dysfluencies with MFCC and LPCC features [PDF]

Expert Systems With Applications, 2012
The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech samples from UCLASS are used for our analysis. The stuttered events are identified through manual segmentation and used for feature
Hariharan Muthusamy, Sazali Yaacob, Lim Sin Chee   +2 more
exaly   +4 more sources

Algorithm of Abnormal Audio Recognition Based on Improved MFCC [PDF]

Procedia Engineering, 2012
Characteristics extraction has a great effect on the audio training and recognition in the audio recognition system. MFCC algorithm is a typical characteristics extraction method with stable performance and high recognition rate.
Xiaoli Cao
exaly   +2 more sources