Iris Lorente | Searcharxiv

arXiv2026

AILive Mixer: A Deep Learning based Zero Latency Automatic Music Mixer for Live Music Performances

In this work, we present a deep learning-based automatic multitrack music mixing system catered towards live performances. In a live performance, channels are often corrupted with acoustic bleeds of co-located instruments. Moreover, audio-visual synchronization is of critical importance thus putting a tight constraint on the audio latency. In this work we primarily tackle these two challenges of handling bleeds in the input channels to produce the music mix with zero latency. Although there have been several developments in the field of automatic music mixing in recent times, most or all previous works focus on offline production for isolated instrument signals and to the best of our knowledge, this is the first end-to-end deep learning system developed for live music performances. Our proposed system currently predicts mono gains for a multitrack input, but its design along with the precedent set in past works, allows for easy adaptation to future work of predicting other relevant music mixing parameters.

eess.AS↗

arXiv2020

Do CNNs solve the CT inverse problem?

Objective: This work examines the claim made in the literature that the inverse problem associated with image reconstruction in sparse-view computed tomography (CT) can be solved with a convolutional neural network (CNN). Methods: Training and testing image/data pairs are generated in a dedicated breast CT simulation for sparse-view sampling, using two different object models. The trained CNN is tested to see if images can be accurately recovered from their corresponding sparse-view data. For reference, the same sparse-view CT data is reconstructed by the use of constrained total-variation (TV) minimization (TVmin), which exploits sparsity in the gradient magnitude image (GMI). Results: Using sparse-view data from images either in the training or testing set, there is a significant discrepancy between the image obtained with the CNN and the image that generated the data. For the same simulated scanning conditions, TVmin is able to accurately reconstruct the test image. Conclusion: We find that the sparse-view CT inverse problem cannot be solved for the particular published CNN-based methodology that we chose and the particular object model that we tested. Furthermore, this negative result is obtained for conditions where TVmin is able to recover the test images. Significance: The inability of the CNN to solve the inverse problem associated with sparse-view CT, for the specific conditions of the presented simulation, draws into question similar unsupported claims being made for the use of CNNs to solve inverse problems in medical imaging.

physics.med-ph↗