Speech separation has evolved into a classification problem, making it a critical area of study. It has been extensively researched in the field of signal processing for several decades, with data-driven methods gaining significant attention in recent years. The primary goal of speech separation is to extract the target speech from background noise and interference. This task is fundamental in signal processing and finds applications in areas such as hearing aids, mobile communications, robust automatic speech recognition, and speaker identification.
The human auditory system excels at isolating a specific voice from a noisy environment, such as a cocktail party, where multiple voices and ambient sounds are present. This ability inspired the term "cocktail party problem," introduced by Cherry in 1953. Despite this natural capability, creating an automated system that matches human performance remains a challenge. In his 1953 book, Cherry noted that no machine could solve the cocktail party problem at the time, a statement that held true for over six decades until recent advancements began to change the landscape.
Speech separation plays a vital role in daily communication, especially when dealing with unwanted noise or reverberation. Humans can effortlessly distinguish between desired speech and background disturbances, but replicating this in machines is complex. Traditional approaches include speech enhancement and computational auditory scene analysis (CASA). Speech enhancement focuses on estimating and removing noise from mixed signals, while CASA mimics how humans group sounds based on cues like pitch and onset.
With the use of multiple microphones, array-based methods such as beamforming are employed to enhance signals coming from specific directions. These techniques improve speech clarity by reducing interference from other sources. However, their effectiveness diminishes in environments with overlapping sound sources or strong reverberation.
In recent years, deep learning has revolutionized speech separation by treating it as a supervised learning problem. Techniques like time-frequency masking and ideal binary masks have enabled more accurate separation of speech from noise. Supervised algorithms now leverage large datasets and advanced neural networks to achieve state-of-the-art results.
This paper explores various aspects of speech separation, including mono and array-based methods, training objectives, and feature learning. It also discusses challenges related to generalization and real-world application scenarios. The article includes visual representations of different DNN architectures and training outcomes, highlighting the progress made in this field.
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