Audio Signal Processing in the 21st Century: The important outcomes of the past 25 years

Audio signal processing has passed many landmarks in its development as a research topic. Many are well known, such as the development of the phonograph in the second half of the 19th century and technology associated with digital telephony that burgeoned in the late 20th century and is still a hot topic in multiple guises. Interestingly, the development of audio technology has been fueled not only by advancements in the capabilities of technology but also by high consumer expectations and customer engagement. From surround sound movie theaters to the latest in-ear devices, people love sound and soon build new audio technology into their daily lives as an essential and expected feature.

 

Some of the major outcomes of the research in audio and acoustic signal processing (AASP) prior to 1997 were summarized in a landmark paper published on the occasion of the 50th anniversary of the IEEE Signal Processing Society (SPS) [1]. At that time, the vast majority of the work was driven by the objective to build models that capture the essential characteristics of the analyzed audio signal and to represent it with a limited set of parameters and components. The field has now evolved beyond the essential characteristics explored in the past. For instance, a wide variety of speech/audio signal models have since been proposed and, in particular, around signal decomposition/factorization models and sparse signal representations. Nevertheless, the entire research domain covered by the IEEE Technical Committee (TC) on AASP is witnessing a paradigm shift toward data-driven methods based on machine learning and, especially, deep learning.