Table 1 Classification of environmental (micro plastics Category Description Classification Environmental plastics are a very heterogeneous group of litter that can be characterized by various descriptors.
Building on the operations strategy (structural vs. infrastructural elements), Klassen and Whybark (1999) introduced a classification of environmental technologies into three mutually exclusive groups.
During recent years, the automatic classification of environmental sounds has gained much attention with application to content-based multimedia indexing and retrieval [33 35].
Hence, this review paper presents several time-frequency-based techniques that has been used to provide efficient extraction of information from audio signals for the purpose of audio coding, music classification, classification of environmental sounds, audio fingerprinting, and watermarking.
In line with the above 3 application areas, this paper presents and discusses a TF-based audio coding scheme, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking.
Within the restricted framework given by the scope of the primary research upon which the present study is based (see Section 2), the final aim is also to automate indexing and classification of environmental sounds.
With respect to real world conditions, another trend of work has been directed to classification of environmental sound using only statistical features, that is, without attempting to identify or isolate sound events [12].
HMMs have become very popular in the speech processing community since the 1980s and today, represent an established method for speech recognition applications[28] as well as for other similar contexts (e.g., speaker recognition or classification of environmental noise).
A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.
This leads to the constitution of a perceptually validated sound corpus with regard to the motor, instrument-like, and impact categories, which is now large and representative enough to consider the conception and validation of a predictive tool for automatic classification of environmental sounds in these three categories.
