Instantaneous Frequency Selective Filtering Using Ensemble Empirical Mode Decomposition

Abstract

In this paper, novel instantaneous frequency (IF) selective high-pass, low-pass and band-pass filtering techniques for multicomponent signals are developed using the ensemble empirical mode decomposition algorithm (EEMD). The EEMD algorithm is based on the property that the empirical mode decomposition algorithm acts on fractional Gaussian noise as a dyadic filter bank of constant-Q band-pass filters. Unlike pre-determined sub-band filtering, the filter-bank structure observed for EEMD applies locally to a signal. In the proposed techniques, frequency translation is carried out to ensure that the ratio of edge IFs of the pass-band and the stop-band is such that the EEMD algorithm will be able to distinguish between the components in the two bands. Also, EEMD approach is applied with band-limited white noise so that the signal components in the pass band and the stop band are extracted in different intrinsic mode functions. Simulations are used to demonstrate the efficacy of the proposed filtering algorithms and to compare their performance with conventional filtering techniques. The performance of the filters is assessed subjectively and in terms of objective criteria in presence of noise. The proposed filtering technique is also applied on a real speech signal to isolate speech resonance signals in accordance with the AM-FM model of speech.

KEYWORDS:

• AM-FM model
• Ensemble empirical mode decomposition algorithm
• Filters
• Instantaneous formant frequency
• Instantaneous frequency

Additional information

Notes on contributors

Rinki Gupta

Rinki Gupta received her PhD in signal processing from the Centre for Applied Research in Electronics (CARE), Indian Institute of Technology (IIT), Delhi in 2014. Thereafter, she was working on a project funded by Ministry of Defence at IIT Delhi. She joined Amity School of Engineering and Technology in Nov 2015. She is also the principal investigator in a project sanctioned by SERB, DST. Her present research interests include time frequency analysis, speech and audio signal processing and multi-sensor data fusion.

Arun Kumar

Arun Kumar received the BTech, MTech and PhD degrees, all in electrical engineering, from the Indian Institute of Technology, Kanpur. He was a visiting research scholar at the University of California, Santa Barbara, from 1994 to 1996. He joined the Centre for Applied Research in Electronics, Indian Institute of Technology, Delhi in 1997, where he is currently professor. His research interests are the fields of digital signal processing algorithms and systems, underwater and air acoustics, human and machine speech communications, and multi-sensor data fusion. He is an inventor of 11US patent applications (5 granted and 6 pending). He has published over 100 papers in refereed journals and conferences, and supervised 13 PhD theses. He has also supervised 55 funded R&D projects from government and industry. These have led to 20 technology and know-how transfers. Email: [email protected]

Rajendar Bahl

Rajendar Bahl has a BTech (honours) in electronics and electrical communication engineering from IIT Kharagpur and PhD (EE) from IIT Delhi. He joined the Centre for Applied Research in Electronics, IIT Delhi in 1974, where he is now emeritus professor. He has participated in various national bodies of the Department of Electronics, Ministry of Information Technology, and the Defence Research & Development Organisation. He was jointly awarded the 1982 Invention Award of the National Research & Development Corporation for the indigenous development of a Digital Control System. He won a Senior Fellowship Award of the US National Research Council during 1989-1990 at the Naval Postgraduate School, Monterey. He has also been a visiting professor in the field of bio-acoustics at the Institute of Industrial Science, University of Tokyo, Japan during 2002-2004. His research interests include sonar design, sensor system design, acoustical imaging and biosonar. Email: [email protected]