Choice of Mel filter bank in computing MFCC of a resampled speech

Kopparapu, Sunil Kumar; Laxminarayana, M

doi:10.1109/isspa.2010.5605491

Cited by 56 publications

(25 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of these are, for example, the specific type of task to be performed, the characteristics of the data, the application domain and the algorithmic and efficiency requirement ( Guyon et al, 2006 ). For instance, traditional choices of features in the context of IR are those obtained by the SIFT ( Lowe, 2004 ) and SURF ( Bay et al, 2008 ) algorithms, whereas mel-cepstral coefficients ( Davis and Mermelstein, 1980 ; Kopparapu and Laxminarayana, 2010 ) are typically chosen in speech recognition applications.…”

Section: Artificial Intelligence-based Prognostic and Health Managemementioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

View full text Add to dashboard Cite

Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

show abstract

Section: Artificial Intelligence-based Prognostic and Health Managemementioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…The calculation of FBEs are as follows:

where L is the length of the filter [ 29 ]. Also,

is obtained using the proposed formula by [ 44 ]

where

is the discrete cosine transform. …”

Section: Appendix A1mentioning

confidence: 99%

3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms

Hajarolasvadi

Demirel

2019

Entropy

130

View full text Add to dashboard Cite

Detecting human intentions and emotions helps improve human–robot interactions. Emotion recognition has been a challenging research direction in the past decade. This paper proposes an emotion recognition system based on analysis of speech signals. Firstly, we split each speech signal into overlapping frames of the same length. Next, we extract an 88-dimensional vector of audio features including Mel Frequency Cepstral Coefficients (MFCC), pitch, and intensity for each of the respective frames. In parallel, the spectrogram of each frame is generated. In the final preprocessing step, by applying k-means clustering on the extracted features of all frames of each audio signal, we select k most discriminant frames, namely keyframes, to summarize the speech signal. Then, the sequence of the corresponding spectrograms of keyframes is encapsulated in a 3D tensor. These tensors are used to train and test a 3D Convolutional Neural network using a 10-fold cross-validation approach. The proposed 3D CNN has two convolutional layers and one fully connected layer. Experiments are conducted on the Surrey Audio-Visual Expressed Emotion (SAVEE), Ryerson Multimedia Laboratory (RML), and eNTERFACE’05 databases. The results are superior to the state-of-the-art methods reported in the literature.

show abstract

“…For each frame, FamilyLog first calculates its energy spectrum from 80 Hz to 8 kHz with the Fast Fourier Transform (FFT) [32]. Then the resulting spectrum is transformed into 21 energy channels by applying Mel Filters [21][25][30]. The energy of channel i will be represented as e i hereafter.…”

Section: System Designmentioning

confidence: 99%

FamilyLog: A mobile system for monitoring family mealtime activities

Xing

Tian³

et al. 2017

2017 IEEE International Conference on Pervasive Computing and Communications (PerCom)

View full text Add to dashboard Cite

Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog – a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.

show abstract

Choice of Mel filter bank in computing MFCC of a resampled speech

Cited by 56 publications

References 5 publications

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms

FamilyLog: A mobile system for monitoring family mealtime activities

Contact Info

Product

Resources

About