spectrotemporal

Spectrotemporal is an adjective describing phenomena that vary with respect to both frequency content and time. In practice, spectrotemporal analysis characterizes signals in a joint time–frequency domain, capturing how the spectrum of a signal evolves over time.

Two-dimensional representations such as spectrograms summarize energy as a function of time and frequency and are widely used in engineering and science. These representations are often obtained with the short-time Fourier transform; other approaches include wavelet-based time–frequency methods. In addition to visualization, spectrotemporal features support tasks like speech recognition, music information retrieval, and audio encoding.

In neuroscience, spectrotemporal concepts are central to modeling auditory processing. The spectrotemporal receptive field (STRF) is a commonly used linear model that characterizes how a neuron responds to patterns of energy across time and frequency. STRFs are estimated from neural responses to natural sounds and form the basis of many studies of auditory perception. The broader idea includes spectrotemporal modulation, describing how sensitivity to combinations of spectral content and time progression influences processing.

Applications span engineering and science: speech and language processing, music analysis, environmental sound classification, hearing aids and cochlear implant research, and fundamental studies of perception and neural encoding.