The ability for humans to navigate integrates vision with acoustics, supporting judgement for motor-based judgements towards safe movement. Similar importance is found in acoustics for service robots concerning ensuring their and their environments’ safety. Most modern systems cannot accurately process time-dependent audio input streams, thereby reducing their robots’ ability to navigate while increasing vulnerability. For instance, in a bustling, under construction airport, a robot would experience interference from direct light rays affecting its camera-based navigation, increasing accident risks. In this respect, a hybrid framework that incorporates Long Short-Term Memory (LSTM) and Time-Delay Neural Networks (TDNN) improves the auditory perception of robots. The proposed model permits an accurate classification of time-dependent sounds, enabling distinction of specific events from ambient noises and incorporates considerations for distance in terms of sounds to navigate. Experimental results demonstrate that the proposed model achieved 90% classification accuracy, outperforming the traditional standalone TDNN and LSTM models with an accuracy of 89% and 82%, respectively. The proposed framework allows processing information in terms of time efficiency while being resourceful, hence less wasteful and faster than more traditional models. Since these are service robots, the proposed process would be combined with movement, reducing latency between the processes. Smoother performance and experience from the users will ensure with much precision in navigating systems by sound.