Title: AN OPERATOR NORM BASED PASSIVE FILTER PRUNING METHOD FOR EFFICIENT CNNS. Abstract: Convolutional neural networks (CNNs) have shown state-of-the-art performance in various applications. However, CNNs are resource-hungry due to their requirement of high computational complexity and memory storage. Recent efforts toward achieving  computational efficiency in CNNs involve filter pruning methods that eliminate some of the filters in CNNs based on the "importance" of the filters. Existing passive filter pruning methods typically use the entry-wise norm of the filters to quantify filter importance, without considering how well the filter contributes in producing the node output. Under situations where the large number of filters are to be pruned from the network, the entry-wise norm methods always select high entry-wise norm filters as important, and ignore the diversity learned by the other filters that may result in degradation in the performance.  To address this, we present a passive filter pruning method where the filters are pruned based on their contribution in producing output by implicitly considering the operator norm of the filters. The computational cost and memory requirement is reduced significantly by eliminating filters and their corresponding feature maps from the network. Accuracy similar to the original network is recovered by fine-tuning the pruned network. The proposed pruning method gives similar or better performance and recovers accuracy faster during the fine-tuning process than the entry-wise norm-based pruning methods. The efficacy of the proposed pruning method is evaluated on audio scene classification (e.g. TAU Urban Acoustic Scenes 2020) and image classification (MNIST handwritten digit classification). 