Weighted entropy cortical algorithms for isolated Arabic speech recognition

Hajj, Nadine; Awad, Mariette

doi:10.1109/ijcnn.2013.6706753

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…We used a cortical network constituted of 6 layers starting with 2000 columns in the first layer and decreasing this number by half between consecutive layers, each column consisting of 20 nodes. This structure was experimentally proven to perform well on a variety of databases [11]. Simulations were executed using Matlab R2011a, on an Intel core i7 CPU at 2 GHz under Windows 7 operating systems.…”

Section: Methodsmentioning

confidence: 99%

“…The nomenclature we adopt in our work is as follows: W r,t i, j,k represents the weight of the connection between the jth neuron of the ith column of layer r and the kth column of the previous layer (r − 1) during the training epoch t. For the rest of this paper, scalar entities are represented in italic, vector entities in bold, while underlined variables represent matrices [11] …”

Section: Primer On Cortical Algorithmsmentioning

confidence: 99%

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Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

Hajj

Filo

Awad

2017

Complex Intell. Syst.

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With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music composers have always been perceived reserved for trained musical ears. While there have been many researchers targeting music genre classification for improved recommendation systems and listener experience, few works have addressed automatic recognition of classical piano composers as proposed in this paper. This paper discusses the applicability of n-grams on MIDI music scores coupled with rhythmic features for feature extraction specifically of multi-voice scores. In addition, cortical algorithms (CA) are adapted to reduce the large feature set obtained as well as to efficiently identify composers in a supervised manner. When used to classify unknown composers and capture different styles, our proposed approach achieved a recognition rate of 94.4% on a home grown database of 1197 pieces with only 0.1% of the 231,542 generated features-which motivates follow-on research. The retained most significant features, indeed, provided interesting conclusions on capturing music style of piano composers. B Mariette Awad

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Section: Methodsmentioning

confidence: 99%

Section: Primer On Cortical Algorithmsmentioning

confidence: 99%

Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

Hajj

Filo

Awad

2017

Complex Intell. Syst.

Self Cite

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“…Training these networks occurs in two stages: an unsupervised stage which trains columns to respond to particular features of the input data, and a supervised phase to correct misrepresentations and create robust abstractions of the input data. A full mathematical description of CA can be found in [28], [29].…”

Section: B Computational Models Of the Visual Cortexmentioning

confidence: 99%

On Biologically Inspired Stochastic Reinforcement Deep Learning: A Case Study on Visual Surveillance

Hajj

Awad

2019

IEEE Access

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Here, we present a biologically inspired visual network (BIVnet) for image processing tasks. The proposed model possesses similarities with its neural counterpart and is trained by a stochastic algorithm which employs a partially observable Markov decision process to execute a reinforcement learning strategy. The network was tested on a collection of available datasets in surveillance-related tasks and showed superior performance compared with the state-of-the-art architectures. An average improvement of 15.2% in accuracy on a collection of publicly available image datasets is shown in our experimental results. INDEX TERMSAbandoned luggage detection, deep learning, person identification, visual tasks.

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“…These two classes are found only in Semitic. These phonemes can be grouped according to the articulation of the lips and tongue during speech [18].…”

Section: Arabic Phonologymentioning

confidence: 99%

Unsupervised Phoneme Segmentation Based on Main Energy Change for Arabic Speech

Lachachi

2017

JTIT

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In this paper, a new method for segmenting speech at the phoneme level is presented. For this purpose, author uses the short-time Fourier transform of the speech signal. The goal is to identify the locations of main energy changes in frequency over time, which can be described as phoneme boundaries. A frequency range analysis and search for energy changes in individual area is applied to obtain further precision to identify speech segments that carry out vowel and consonant segment confined in small number of narrow spectral areas. This method merely utilizes the power spectrum of the signal for segmentation. There is no need for any adaptation of the parameters or training for different speakers in advance. In addition, no transcript information, neither any prior linguistic knowledge about the phonemes is needed, or voiced/unvoiced decision making is required. Segmentation results with proposed method have been compared with a manual segmentation, and compared with three same kinds of segmentation methods. These results show that 81% of the boundaries are successfully identified. This research aims to improve the acoustic parameters for all the processing systems of the Arab speech.

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Weighted entropy cortical algorithms for isolated Arabic speech recognition

Cited by 9 publications

References 13 publications

Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

On Biologically Inspired Stochastic Reinforcement Deep Learning: A Case Study on Visual Surveillance

Unsupervised Phoneme Segmentation Based on Main Energy Change for Arabic Speech

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