We propose a novel multi-source direction of arrival (DOA) estimation technique using a convolutional neural network algorithm which learns the modal coherence patterns of an incident soundfield through measured spherical harmonic coefficients. We train our model for individual time-frequency bins in the short-time Fourier transform spectrum by analyzing the unique snapshot of modal coherence for each desired direction. The proposed method is capable of estimating simultaneously active multiple sound sources on a $3$D space using a single-source training scheme. This single-source training scheme reduces the training time and resource requirements as well as allows the reuse of the same trained model for different multi-source combinations. The method is evaluated against various simulated and practical noisy and reverberant environments with varying acoustic criteria and found to outperform the baseline methods in terms of DOA estimation accuracy. Furthermore, the proposed algorithm allows independent training of azimuth and elevation during a full DOA estimation over $3$D space which significantly improves its training efficiency without affecting the overall estimation accuracy.

中文翻译：

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通过混响声场模态相干的模式识别进行多源 DOA 估计

我们提出了一种使用卷积神经网络算法的新型多源到达方向 (DOA) 估计技术，该算法通过测量的球谐系数学习入射声场的模态相干模式。我们通过分析每个所需方向的模态相干性的独特快照，为短时傅立叶变换频谱中的各个时频区间训练我们的模型。所提出的方法能够使用单源训练方案在 $3$D 空间上同时估计多个活跃的声源。这种单源训练方案减少了训练时间和资源需求，并允许对不同的多源组合重复使用相同的训练模型。该方法针对具有不同声学标准的各种模拟和实际噪声和混响环境进行了评估，发现在 DOA 估计精度方面优于基线方法。此外，所提出的算法允许在超过 $3$D 空间的完整 DOA 估计期间独立训练方位角和仰角，这显着提高了其训练效率，而不会影响整体估计精度。