# A Combined Method for Localizing Two Overlapping Acoustic Sources Based on Deep Learning
###### Abstract  
Deep learning approaches for multi-source sound localization face significant challenges, particularly the need for extensive training datasets encompassing diverse spatial configurations to achieve robust generalization. This requirement leads to substantial computational demands, which are further exacerbated when localizing overlapping sources in complex acoustic environments with reverberation and noise. In this paper, a new methodology is proposed for simultaneous localization of two overlapping sound sources in the time-frequency domain in a closed, reverberant environment with a spatial resolution of \(10^{\circ}\) using a small-sized microphone array. The proposed methodology is based on the integration of the sound source separation method with a single-source sound localization model. A hybrid model was proposed to separate the sound source signals received by each microphone in the array. The model was built using a bidirectional long short-term memory (BLSTM) network and trained on a dataset using the ideal binary mask (IBM) as the training target. The modeling results show that the proposed localization methodology is efficient in determining the directions for two overlapping sources simultaneously, with an average localization accuracy of 86.1% for the test dataset containing short-term signals of 500 ms duration with different signal-to-signal ratio values. deep learning; multi-source sound localization; overlapping sound sources; reverberant environment; small-sized microphone array; sound source separation; single-source sound localization; bidirectional long short-term memory; ideal binary mask +
Footnote †: journal: Applied sciences  
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with noise and reverberation. The complexity increases further when multiple active sound sources must be localized simultaneously. The problem of SSL is currently addressed using two main approaches: traditional signal processing methods and deep learning (DL)-based methods. Traditional methods--including multiple signal classification (MUSIC) [5], time difference of arrival (TDOA) [6], delay-and-sum beamformer (DAS) [7], generalized cross-correlation-phase transform (GCC-PHAT) [8], and steered response power-phase transform (SRP-PHAT) [9]--are typically developed under the free-field propagation assumption. Consequently, their performance significantly deteriorates in enclosed acoustic environments characterized by reverberation and noise [10]. DL-based SSL methods offer a key advantage by incorporating acoustic signal characteristics into the training process. This allows them to adapt to diverse and complex acoustic environments, often achieving greater robustness against noise and reverberation than traditional methods [10]. Much of the current research in DL-based SSL focuses on localizing multiple sound sources by framing the problem as a multi-class classification task. Deep neural networks (DNNs) have been designed using novel architectures, combinations of serial and parallel configurations, hyperparameter tuning, and varied input feature sets [11; 12; 13]. For example, in [14] a convolutional neural network (CNN) was employed to detect and localize multiple speakers. However, the model's generalization capability is limited due to training data that do not encompass all possible combinations of acoustic sources. As a result, it cannot effectively localize multiple sources beyond the scenarios represented in the training set. In [15], another CNN-based model was proposed, using multichannel short-time Fourier transform (STFT) phase spectrograms as input features to estimate the azimuth of one or two sources in a reverberant environment. While this model outperformed SRP-PHAT and MUSIC under the evaluated conditions, it assumes that the two active sources do not overlap in the time-frequency domain, a constraint that limits its applicability in real-world scenarios involving simultaneous overlapping sources.