A system for semantic information extraction from mixed soundtracks deploying MARSYAS framework

Mohammed, Duraid Y.; Duncan, Philip J.; Al-Maathidi, Muhammad; Li, Francis F.

doi:10.1109/indin.2015.7281886

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…An audio mixer was developed using the MATLAB code, which blends pre-recorded speech segments with noise according to their signal intensity. The mixing strategy used was empirically verified and published to emulate the best mix of soundtracks [25], [26]. The sound mixer procedure described is being as: firstly, the issue of normalization is addressed in such a way that speech and noise are added in the wanted proportion to avoid misinterpretation.…”

Section: Noisy Datamentioning

confidence: 99%

Robust speaker verification by combining MFCC and entrocy in noisy conditions

2021

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Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.

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Section: Noisy Datamentioning

confidence: 99%

Robust speaker verification by combining MFCC and entrocy in noisy conditions

2021

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“…103 Semantic IE from audio is capable to extract music score and text information through classification and segmentation which are helpful to update and insert music or speech occurrence and analyze arbitrary soundtracks. 104 Recently, the exponential growth of unstructured big data and computational power, ASR is moving toward more advanced and challenging applications such as mobile interaction with voice, voice control in smart systems, and communicative assistance. 105 Limitations of IE techniques for audio content.…”

Section: Audio Content Ie Techniquesmentioning

confidence: 99%

“…The field of acoustic IE is facing challenges such as more accurate feature selection, 97,99 classification of nonexclusive sound and content overlapping. 104 Call centers use audio data for analysis and IE in the form of conversations with clients, music, monitoring, and processing of conversations. Background noise, words overlapping, considering one single voice in crowd, and language ambiguities are open challenges in this domain.…”

Section: Audio Content Ie Techniquesmentioning

confidence: 99%

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

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“…Semantic IE to extract music score and text information using segmentation and classification is achieved by analyzing arbitrary soundtracks and timestamp the occurrence of music and speech [35]. An IE approach is required for integrated detection and verification from speech which can be useful for speech analysis, speech recognition, speaker and language recognition [36].…”

Section: Information Extraction From Audio Datamentioning

confidence: 99%

Information Extraction from Multifaceted Unstructured Big Data

2019

IJRTE

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In the era of digital globalization, huge volume and variety of data are being produced at a very high rate. Every day, the world is producing around 2.5 quintillion bytes of data. According to IDC, by 2020, over 40 zettabytes of data will be generated and reproduced. Digital data have become a deluge, overwhelming in every field of information technology (IT), business, science and engineering. These fields are shifting to smart and advanced technologies such as smart manufacturing industries, data-aware medical sciences, and other smart applications. These applications are facilitating the industries in context of data-driven decision making, big data storage, and complex analysis of large data sets. Also, these applications are contributing to generate big data deluge where a variety of data necessitate the industries to use advanced IT approaches. 95% of the digital universe is unstructured data. It is rich data as it contains information that can play a vital role to improve big data analytics. The heterogeneity, complexity, lack of structured information, poor quality and scalability of unstructured data generates difficulties in adapting traditional information extraction techniques. Information extraction can play a vital role in transformation of unstructured data into useful information. A multistep pipeline with data preprocessing steps, extraction methods and representation are utmost requirement to improve the unstructured data analytics. In this regard, this paper presents a short review of information extraction process w.r.t. input data type, extraction methods with their corresponding techniques, and representation of extracted information. The issues with unstructured data and the challenges to information extraction from multifaceted unstructured big data as well as the future research directions have also been discussed

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A system for semantic information extraction from mixed soundtracks deploying MARSYAS framework

Cited by 8 publications

References 19 publications

Robust speaker verification by combining MFCC and entrocy in noisy conditions

Robust speaker verification by combining MFCC and entrocy in noisy conditions

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Information Extraction from Multifaceted Unstructured Big Data

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