Recognition of isolated words using Zernike and MFCC features for audio visual speech recognition

Borde, Prashant; Varpe, Amarsinh B.; Manza, Ramesh R.; Yannawar, Pravin

doi:10.1007/s10772-014-9257-1

Cited by 48 publications

(19 citation statements)

References 13 publications

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“…Given

V_{n, m} (x, y)

, we can calculate the ZM

Z_{n, m} (I)

of the n ‐th order and with the repetition number of m through Equation () (Borde et al, ) for an arbitrary image I ( x , y ) with the size of p × q pixels. In the calculation, I ( x , y ) can be alternatively viewed as a function defined over the integer space of

x \in [0, p]

and

y \in [0, q]

.…”

Section: The New Methodsmentioning

confidence: 99%

“…These drawbacks can be resolved by region-based shape feature extraction methods, which represent an image as one or a collection of interior shape regions. Mainstream methods for deriving region-based shape descriptors include image moment-based descriptors (Borde, Varpe, Manza, & Yannawar, 2014;Chen & Sun, 2010;Papakostas, Boutalis, Karras, & Mertzios, 2007), gridbased descriptors (Lu & Sajjanhar, 1999), and distributionbased shape descriptors. Distribution-based shape descriptors are derived according to histograms of points or subregions of interest inside an image, for example, scaleinvariant feature descriptors (Lowe, 2004), oriented gradient-based descriptors (Dalal & Triggs, 2005;Mikolajczyk & Schmid, 2005), and sped-up robust features (Bay, Ess, Tuytelaars, & Van, 2008).…”

Section: Related Workmentioning

confidence: 99%

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A new method for retrieving batik shape patterns

Yuan

2018

Asso for Info Science & Tech

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Batik as a traditional art is well regarded due to its high aesthetic quality and cultural heritage values. It is not uncommon to reuse versatile decorative shape patterns across batiks. General‐purpose image retrieval methods often fail to pay sufficient attention to such a frequent reuse of shape patterns in the graphical compositions of batiks, leading to suboptimal retrieval results, in particular for identifying batiks that use copyrighted shape patterns without proper authorization for law‐enforcement purposes. To address the lack of an optimized image retrieval method suited for batiks, this study proposes a new method for retrieving salient shape patterns in batiks using a rich combination of global and local features. The global features deployed were extracted according to the Zernike moments (ZMs); the local features adopted were extracted through curvelet transformations that characterize shape contours embedded in batiks. The method subsequently incorporated both types of features via matching a weighted bipartite graph to measure the visual similarity between any pair of batik shape patterns through supervised distance metric learning. The derived similarity metric can then be used to detect and retrieve similar shape patterns appearing across batiks, which in turn can be employed as a reliable similarity metric for retrieving batiks. To explore the usefulness of the proposed method, the performance of the new retrieval method is compared against that of three peer methods as well as two variants of the proposed method. The experimental results consistently and convincingly demonstrate that the new method indeed outperforms the state‐of‐the‐art methods in retrieving salient shape patterns in batiks.

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“…Given

V_{n, m} (x, y)

, we can calculate the ZM

Z_{n, m} (I)

x \in [0, p]

and

y \in [0, q]

.…”

Section: The New Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A new method for retrieving batik shape patterns

Yuan

2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Some existingworks, for instant, Agus Harjokoexamined voices by using MFCC (Abriyono and Harjoko, 2012) to extract voice features. The same research have also been performed by Prasad (Borde, 2015) andChan (Chan et al, 2016) by using neural network.…”

Section: Introductionmentioning

confidence: 94%

Voice Identification Using Neural Network And Mel Frequency Cepstrum Coefficients

Gms¹,

Fahmi²,

Pinem³

et al. 2019

Proceedings of the Proceedings of the 2nd International Conference on Advance and Scientific Innovation, ICASI 2019, 18 July, B

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Voice as a communication media for human and computer communication is developed by using voice recognition/identification technology. One of its applications is differentiate between male and female voices. Some speech recognition research have been proposed in disseminating male and female voices, this paper performs male and female voice extraction by using Mel Frequency Cestrum Coefficients (MFCC) as the characteristic vector in back propagation artificial neural network (ANN). The weight change cycle or EPOCH (Exponential Decay) is used to initialize male and female voice identification by using multiple recorded voices. As results, the female voice identification is better than of male with error less than 1.

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“…iris and eye movement) [72]. [73], [74], [75], [76], [77], [78], [79], [80] Behavioral (Gait, Signature, Handwriting) Sparse reconstruction based metric learning, Gradient local binary patterns, longest run feature [81], [82] Bioacoustics Pitch, Zernike moment [83], [84] Bio-Signal (EEG, ERG, ECG, EMG, EOG, GSR, MEG, MCG, MMG)…”

Section: )mentioning

confidence: 99%

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

et al. 2019

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Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being-acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems.

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Recognition of isolated words using Zernike and MFCC features for audio visual speech recognition

Cited by 48 publications

References 13 publications

A new method for retrieving batik shape patterns

A new method for retrieving batik shape patterns

Voice Identification Using Neural Network And Mel Frequency Cepstrum Coefficients

Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review

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