Exact(41)
This paper presents a methodology that incorporates temporal feature integration for automated generalized sound recognition.
It consists of two different stages: short-time feature extraction and temporal feature integration.
[±temporal]: the temporal feature denotes events that are involved within the timeline.
As illustrated below, and and listing are other subordinate types discriminated by the temporal feature.
The proposed system not only uses diverse groups of sound parameters but also employs the advantages of temporal feature integration.
The use of grid flow representation, per-frame normalization and temporal feature accumulation enhances the robustness of our new representation.
Similar(19)
[23] AMC Instantaneous temporal feature-based modulation FANN (4,7,5) the overall success rate at -5 dB is 99.65%.
Cyclic spectral analysis [17], wavelet cyclic features [21], temporal feature-based modulation [22, 23], carrier frequency and baud rate [24], and continuous wavelet transform (CWT) [25] are some examples of these features.
Table 7 Comparison between different works in terms of features, ANN model and the achieved SNR with the recognition accuracy Reference Application Applied features Type of ANN Recognition accuracy [22] AMC Instantaneous temporal feature-based FANN (5,19,8) and PANN (5,1800,8) Overall success rate at -5 db are 65.63% and 55.5% respectively.
Figure 5 Processing stages of the non-linear spectro-temporal feature extraction.
In this section, we discuss the spatio-temporal feature of the fall, followed by the sensing model design.
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