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Polyphonic sound event detection and direction-of-arrival estimation require different input features from audio signals. While sound event detection mainly relies on time-frequency patterns, direction-of-arrival estimation relies on magnitude or phase differences between microphones. Previous approaches use the same input features for sound event detection and direction-of-arrival estimation, and train the two tasks jointly or in a two-stage transfer-learning manner.

In this paper, we present a method to handle data imbalance for classification with neural networks, and apply it to acoustic event detection (AED) problem. The common approach to tackle data imbalance is to use class-weights in the objective function while training. An existing more sophisticated approach is to map the input to clusters in an embedding space, so that learning is locally balanced by incorporating inter-cluster and inter-class margins. On these lines, we propose a method to learn the embedding using a novel objective function, called triple-header cross entropy.

In this paper, we focus on the problem of content-based retrieval for
audio, which aims to retrieve all semantically similar audio recordings
for a given audio clip query. This problem is similar to the
problem of query by example of audio, which aims to retrieve media
samples from a database, which are similar to the user-provided example.
We propose a novel approach which encodes the audio into
a vector representation using Siamese Neural Networks. The goal is
to obtain an encoding similar for files belonging to the same audio

In this paper, we focus on the problem of content-based retrieval for
audio, which aims to retrieve all semantically similar audio recordings
for a given audio clip query. This problem is similar to the
problem of query by example of audio, which aims to retrieve media
samples from a database, which are similar to the user-provided example.
We propose a novel approach which encodes the audio into
a vector representation using Siamese Neural Networks. The goal is
to obtain an encoding similar for files belonging to the same audio

In this paper we present the INESC Key Detection (IKD) system which incorporates a novel method for dynamically biasing key mode estimation using the spatial displacement of beat-synchronous Tonal Interval Vectors (TIVs). We evaluate the performance of the IKD system at finding the global key on three annotated audio datasets and using three key-defining profiles.

We approach cover song identification using a novel time-series representation of audio based on the 2DFT. The audio is represented as a sequence of magnitude 2D Fourier Transforms (2DFT). This representation is robust to key changes, timbral changes, and small local tempo deviations. We look at cross-similarity between these time-series, and extract a distance measure that is invariant to music structure changes. Our approach is state-of-the-art on a recent cover song dataset, and expands on previous work using the 2DFT for music representation and work on live song recognition.

The most efficient signal edge-preserving smoothing filters, e.g., for denoising, are non-linear. Thus, their acceleration is challenging and is often done in practice by tuning filters parameters, such as increasing the width of the local smoothing neighborhood, resulting in more aggressive smoothing of a single sweep at the cost of increased edge blurring. We propose an alternative technology, accelerating the original filters without tuning, by running them through a conjugate gradient method, not affecting their quality.