A processor implemented method, comprising: transmitting, via a plurality of speakers, a plurality of modulated signals to a user; receiving, via a plurality of microphones, a plurality of reflected signals from the user, in response to the plurality of transmitted modulated signals; processing, via a Channel Impulse Response, CIR, estimator, the plurality of transmitted modulated signals and the plurality of reflected signals using a sparsity prior serving as a constraint to obtain a plurality of CIR images, wherein processing the plurality of transmitted modulated signals and the plurality of reflected signals using the sparsity prior serving as a constraint comprises:estimating the plurality of CIR images by solving the optimization equation using iterative shrinkage threshold algorithm, ISTA, given by: h j = arg min h j y j − X h j 2 2 + λ h j 1, wherein h j is a tap CIR of the reflected signal from the jth microphone, y j is received signal at the jth microphone, X is a matrix of dimension P × L, wherein L denotes number of taps and P is chosen such that P+L=480, and λ is a hyper-parameter which controls between mean square error, MSR, and the sparsity prior serving as the constraint; and recognizing, via a Spiking Neural Network, SNN, a gesture performed by the user based on the plurality of CIR images, wherein the Spiking Neural Network is obtained by: training a Convolutional Neural Network, CNN, using training data comprising a plurality of CIR images corresponding to one or more users to obtain a trained CNN; quantizing the trained CNN to obtain a quantized CNN; and converting the quantized CNN to the SNN, and wherein the quantized CNN is converted to the SNN by performing an approximate matching of a corresponding output of an CNN neuron comprised in the CNN to a firing rate of a spiking neuron comprised in the SNN. transmitting, via a plurality of speakers, a plurality of modulated signals to a user; receiving, via a plurality of microphones, a plurality of reflected signals from the user, in response to the plurality of transmitted modulated signals; processing, via a Channel Impulse Response, CIR, estimator, the plurality of transmitted modulated signals and the plurality of reflected signals using a sparsity prior serving as a constraint to obtain a plurality of CIR images, wherein processing the plurality of transmitted modulated signals and the plurality of reflected signals using the sparsity prior serving as a constraint comprises:estimating the plurality of CIR images by solving the optimization equation using iterative shrinkage threshold algorithm, ISTA, given by: h j = arg min h j y j − X h j 2 2 + λ h j 1, wherein h j is a tap CIR of the reflected signal from the jth microphone, y j is received signal at the jth microphone, X is a matrix of dimension P × L, wherein L denotes number of taps and P is chosen such that P+L=480, and λ is a hyper-parameter which controls between mean square error, MSR, and the sparsity prior serving as the constraint; and recognizing, via a Spiking Neural Network, SNN, a gesture performed by the user based on the plurality of CIR images, wherein the Spiking Neural Network is obtained by: training a Convolutional Neural Network, CNN, using training data comprising a plurality of CIR images corresponding to one or more users to obtain a trained CNN; quantizing the trained CNN to obtain a quantized CNN; and converting the quantized CNN to the SNN, and wherein the quantized CNN is converted to the SNN by performing an approximate matching of a corresponding output of an CNN neuron comprised in the CNN to a firing rate of a spiking neuron comprised in the SNN. quantizing the trained CNN to obtain a quantized CNN; and converting the quantized CNN to the SNN, and wherein the quantized CNN is converted to the SNN by performing an approximate matching of a corresponding output of an CNN neuron comprised in the CNN to a firing rate of a spiking neuron comprised in the SNN.