Speech separation has evolved into a classification problem, making it increasingly important in the field of signal processing. For several decades, researchers have explored various approaches to separate target speech from background noise. With the rise of data-driven methods, especially in speech processing, this area has seen significant advancements.
The primary goal of speech separation is to isolate the desired speech signal from unwanted interference such as noise, other voices, or reverberation. This task is fundamental and widely applicable, with uses in hearing aids, mobile communications, robust automatic speech recognition, and speaker identification. Humans can effortlessly focus on one voice amidst a noisy environment—like a cocktail party—making this challenge famously known as the "cocktail party problem," first introduced by Cherry in 1953.
Language is the most essential form of human communication, and being able to separate speech from background interference is crucial for effective interaction. However, creating an automated system that matches the human auditory system remains a complex challenge. In his 1953 book, Cherry noted, “There is no machine that can solve the 'cocktail party problem' so far.†Despite recent progress, his observation still holds true in many scenarios.
Speech separation techniques vary based on the number of microphones used. Monaural methods rely on a single microphone, while array-based methods use multiple microphones. Traditional monaural approaches include speech enhancement and computational auditory scene analysis (CASA). Speech enhancement involves estimating and removing noise from the mixed signal, often using spectral subtraction. CASA mimics human perception by grouping sounds based on cues like pitch and onset.
Array-based methods, such as beamforming, use spatial information to enhance signals from a specific direction. However, these techniques are less effective when sources are close together or in environments with strong echoes.
Recently, supervised learning has emerged as a powerful approach, inspired by time-frequency masking concepts. Ideal binary masks (IBMs) guide the training process, turning speech separation into a binary classification task. This method has shown promising results in improving speech intelligibility in noisy conditions.
Over the past decade, deep learning has significantly advanced speech separation, leading to state-of-the-art performance. Supervised algorithms, including those based on DNNs and LSTMs, have become central to modern research. This paper reviews key components of these systems, such as learning models, training objectives, and acoustic features.
To avoid confusion, we clarify some terms: speech separation refers to isolating target speech from any interfering signals, while speech enhancement focuses on removing non-speech noise. When dealing with multiple speakers, the term "speaker separation" is used.
Several figures illustrate different training targets, performance metrics, and model architectures, highlighting the effectiveness of deep learning in speech separation tasks. These visualizations provide insights into how various algorithms perform under different conditions.
Ei48 Transformer,Ei48 4Ohm Audio Transformer,48 Volt Transformer,Ei48 20W Audio Line Transformer,led transformer
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