A computer-implemented method for identifying at least one audio signal, the method comprising the steps of: receiving audio data at a receiver module from at least one audio sensor; and processing the audio data using a signal recognition module; wherein processing the audio data using the signal recognition module comprises: based on the received audio data, determining at least one of: one or more time-varying vector arrays of octave band energies, and one or more time-varying vector arrays of fractional octave band energies; determining one or more time-varying vector arrays of Mel-Frequency Cepstral Coefficients (MFCC) values based on the received audio data; generating audio feature image data based on the one or more time-varying vector arrays of MFCC values, and at least one of: the one or more time-varying vector arrays of octave band energies, and the one or more time-varying vector arrays of fractional octave band energies; wherein the audio feature image data is generated by combining vector values of the one or more time-varying vector arrays of MFCC values, and at least one of: vector values of the one or more time-varying vector arrays of octave band energies, and vector values of the one or more time-varying vector arrays of fractional octave band energies into a single matrix; and identifying at least one audio signal using a first model based on the audio feature image data; wherein the first model comprises an image recognition model to identify a pattern in the audio feature image data to identify the at least one audio signal. receiving audio data at a receiver module from at least one audio sensor; and processing the audio data using a signal recognition module; wherein processing the audio data using the signal recognition module comprises: based on the received audio data, determining at least one of: one or more time-varying vector arrays of octave band energies, and one or more time-varying vector arrays of fractional octave band energies; determining one or more time-varying vector arrays of Mel-Frequency Cepstral Coefficients (MFCC) values based on the received audio data; generating audio feature image data based on the one or more time-varying vector arrays of MFCC values, and at least one of: the one or more time-varying vector arrays of octave band energies, and the one or more time-varying vector arrays of fractional octave band energies; wherein the audio feature image data is generated by combining vector values of the one or more time-varying vector arrays of MFCC values, and at least one of: vector values of the one or more time-varying vector arrays of octave band energies, and vector values of the one or more time-varying vector arrays of fractional octave band energies into a single matrix; and identifying at least one audio signal using a first model based on the audio feature image data; wherein the first model comprises an image recognition model to identify a pattern in the audio feature image data to identify the at least one audio signal. based on the received audio data, determining at least one of: one or more time-varying vector arrays of octave band energies, and one or more time-varying vector arrays of fractional octave band energies; one or more time-varying vector arrays of octave band energies, and one or more time-varying vector arrays of fractional octave band energies; determining one or more time-varying vector arrays of Mel-Frequency Cepstral Coefficients (MFCC) values based on the received audio data; generating audio feature image data based on the one or more time-varying vector arrays of MFCC values, and at least one of: the one or more time-varying vector arrays of octave band energies, and the one or more time-varying vector arrays of fractional octave band energies; the one or more time-varying vector arrays of octave band energies, and the one or more time-varying vector arrays of fractional octave band energies; wherein the audio feature image data is generated by combining vector values of the one or more time-varying vector arrays of MFCC values, and at least one of: vector values of the one or more time-varying vector arrays of octave band energies, and vector values of the one or more time-varying vector arrays of fractional octave band energies vector values of the one or more time-varying vector arrays of octave band energies, and vector values of the one or more time-varying vector arrays of fractional octave band energies into a single matrix; and identifying at least one audio signal using a first model based on the audio feature image data; wherein the first model comprises an image recognition model to identify a pattern in the audio feature image data to identify the at least one audio signal.