Modern audio processing networks are commonly deployed on accelerators whose peak throughput is obtained through dense linear algebra, whereas conventional acoustic frontends -- a Short-Time Fourier Transform (STFT) followed by sparse Mel aggregation -- remain structurally heterogeneous. This mismatch can introduce memory-bandwidth, dispatch, and intermediate-allocation overheads on contemporary accelerator backends. This work introduces MelT, a single-stage frontend framework in which Mel-spaced Non-Uniform Discrete Fourier Transform (NDFT) bases are precomputed and applied to time-domain acoustic frames through dense General Matrix Multiplication (GEMM) operations. The contribution is not the NDFT operator itself; rather, it is the formulation of Mel-spaced NDFT projection as a GEMM-native audio frontend and its evaluation as a hardware-efficient alternative to conventional STFT+Mel pipelines. Evaluated across platforms ranging from Apple A18 Pro edge hardware to NVIDIA H100 datacenter acceleration, MelT attains up to a $3.75\times$ speedup in inference latency and a $3.52\times$ reduction in energy consumption while maintaining downstream classification accuracy.

翻译：现代音频处理网络通常部署在通过密集线性代数获得峰值吞吐量的加速器上，而传统声学前端——短时傅里叶变换（STFT）后接稀疏梅尔聚合——在结构上仍保持异构性。这种不匹配会在当代加速器后端引入内存带宽、调度和中间分配开销。本文提出MelT，一种单阶段前端框架，其中梅尔间隔非均匀离散傅里叶变换（NDFT）基被预先计算，并通过通用矩阵乘法（GEMM）操作应用于时域音频帧。此项贡献不在于NDFT算子本身，而在于将梅尔间隔NDFT投影表述为GEMM原生的音频前端，并将其评估为传统STFT+梅尔管线的硬件高效替代方案。在从Apple A18 Pro边缘硬件到NVIDIA H100数据中心加速器的多平台评估中，MelT在保持下游分类准确率的同时，实现了高达3.75倍的推理延迟加速和3.52倍的能耗降低。