Speech Compression Technique Using Compressive Sensing Theory

In this paper, a new speech compression technique is proposed. This technique applies a Psychoacoustic Model and a general approach for Filter Bank Design using optimization. It is evaluated and compared with a compression technique using a MDCT (Modified Discrete Cosine Transform) Filter Bank of 32 Filters and a Psychoacoustic Model. This evaluation and comparison is performed by calculating bits before and after compression, PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error), SNR (Signal to Noise Ratio) and PESQ (Perceptual evaluation of speech quality) computations. The two techniques are tested and applied to a number of speech signals that are sampled at 8 kHz. The results obtained from this evaluation show that the proposed technique outperforms the second compression technique (based on a Psychoacoustic Model and MDCT filter Bank) in terms of Bits after compression and compression ratio. In fact, the proposed technique yields higher values for the compression ratio than the second compression technique. Moreover, the proposed compression technique presents reconstructed speech signals with acceptable perceptual qualities. This is justified by the values of SNR, PSNR and NRMSE and PESQ.

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Clustering and Compressive Data Gathering for Transmission Efficient Wireless Sensor Networks

Managing Resources for Futuristic Wireless Networks - Advances in Wireless Technologies and Telecommunication ◽

10.4018/978-1-5225-9493-2.ch002 ◽

2021 ◽

pp. 28-55

Author(s):

Utkarsha Sumedh Pacharaney ◽

Ranjan Bala Jain ◽

Rajiv Kumar Gupta

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Compressive Sensing ◽

Data Gathering ◽

Correlated Data ◽

Wireless Sensor ◽

Clustering Methods ◽

Compression Technique ◽

Spatially Correlated ◽

Sensory Data

The chapter focuses on minimizing the amount of wireless transmission in sensory data gathering for correlated data field monitoring in wireless sensor networks (WSN), which is a major source of power consumption. Compressive sensing (CS) is a new in-node compression technique that is economically used for data gathering in an energy-constrained WSN. Among existing CS-based routing, cluster-based methods offer the most transmission-efficient architecture. Most CS-based clustering methods randomly choose nodes to form clusters, neglecting the topology structure. A novel base station (BS)-assisted cluster, spatially correlated cluster using compressive sensing (SCC_CS), is proposed to reduce number of transmissions in and form the cluster by exploiting spatial correlation based on geographical proximity. The proposed BS-assisted clustering scheme follows hexagonal deployment strategy. In SCC_CS, cluster heads are solely involved in data gathering and transmitting CS measurements to BS, saving intra-cluster communication cost, and thus, network life increases as proved by simulation.

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Applying a Speaker-Dependent Speech Compression Technique to Concatenative TTS Synthesizers

IEEE Transactions on Audio Speech and Language Processing ◽

10.1109/tasl.2006.876762 ◽

2007 ◽

Vol 15 (2) ◽

pp. 632-640 ◽

Cited By ~ 3

Author(s):

Chang-Heon Lee ◽

Sung-Kyo Jung ◽

Hong-Goo Kang

Keyword(s):

Speech Compression ◽

Compression Technique

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Combined speech compression and encryption using chaotic compressive sensing with large key size

IET Signal Processing ◽

10.1049/iet-spr.2016.0708 ◽

2018 ◽

Vol 12 (2) ◽

pp. 214-218 ◽

Cited By ~ 15

Author(s):

Maher K. Mahmood Al‐Azawi ◽

Ali M. Gaze

Keyword(s):

Compressive Sensing ◽

Speech Compression

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Two-dimensional satellite image compression using compressive sensing

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i1.pp311-319 ◽

2022 ◽

Vol 12 (1) ◽

pp. 311

Author(s):

Gunasheela Keragodu Shivanna ◽

Haranahalli Shreenivasamurthy Prasantha

Keyword(s):

Image Compression ◽

Compressive Sensing ◽

Satellite Image ◽

Research Community ◽

Reconstruction Accuracy ◽

Promising Technique ◽

Compression Technique ◽

Satellite Imaging ◽

Compression Algorithms ◽

Ground Station

Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recover the entire satellite image from compressive sensing samples. The compression results obtained are compared with the compression results from conventional satellite image compression algorithms. The results demonstrate the increase in reconstruction accuracy as well as higher compression rate in case of compressive sensing-based compression technique.

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