The development of Fuzzy Logic Controllers (FLC) with low error rates and cost effectiveness has been the subject of numerous studies. This paper study goals to the investigation and then implementation an FLC using the readily accessible and reasonably priced Raspberry Pi technology. The FLC used in this work has two inputs, one output, and five Membership Functions (MFs) for each input and output. The FLC goes through two processes, tweaking the MF parameters and tuning input/ output Scaling Factors. The tuning technique makes use of the Genetic Algorithm (GA). The whole set of the FLC probabilities is taken into account as the tuned FLC controller, and then transformed into a lookup table. The Center of Gravity (COG) approach is used to determine the output for the tuned FLC controller. The resulting table is converted into values of digital binary using a specific type of encoder, and then extraction of the set of Boolean functions to apply this tuned circuit. Finally, the Python 3 programming language is used to define the resultant Boolean functions on the Raspberry Pi platform, and then a decoder extracted the appropriate control action from the output. The Benefit of this method is the use of a digital numbering system to define the FLC, which is implemented on Raspberry Pi technology and allows for fuzzified high processing speed output per second. The controller speed has not been unaffected by the quantity for these fuzzy rules.
This study proposes a blind speech separation algorithm that employs a single-channel technique. The algorithm’s input signal is a segment of a mixture of speech for two speakers. At first, filter bank analysis transforms the input from time to time-frequency domain (spectrogram). Number of sub-bands for the filter is 257. Non-Negative Matrix Factorization (NNMF) factorizes each sub-band output into 28 sub-signals. A binary mask separates each sub-signal into two groups; one group belongs to the first speaker and the other to the second speaker. The binary mask separates each sub-signal of the (257×28) 7196 sub-speech signals. That separation cannot identify the speaker. Identification of the sub-signal speaker for each sub-signal is achieved by speaker clustering algorithms. Since speaker clustering cannot process without speaker segmentation, the standard windowed-overlap frames have been used to partition the speech. The speaker clustering process fetches the extracted phase angle from the spectrogram (of the mixture speech) and merges it into the spectrogram (of the recovered speech). Filter bank synthesizes these signals to produce a full-band speech signal for each speaker. Subjective tests denote that the algorithm results are accepted. Objectively, the researchers experimented with 66 mixture chats (6 females and 6 males) to test the algorithm. The average of the SIR test is 11.1 dB, SDR is 1.7 dB, and SAR is 2.8 dB.