Spectrogram

About: Spectrogram is a research topic. Over the lifetime, 5813 publications have been published within this topic receiving 81547 citations.

Experiments on spectrogram reading

Abstract: This paper presents the results of several spectrogram reading experiments that were designed to determine the amount of phonetic information that is contained in the speech signal. The task involved identifying the phonetic content of an unknown utterance only from a visual examination of the spectrogram. In the first experiment, one of the authors attempted to phonetically label spectrograms of normal and anomalous English utterances as well as words in a known carrier phrase. The results, when compared with the transcriptions of three phoneticians who listened to the utterances, indicated an overall agreement of better than 85% on the sentences, and 93% for words in a carrier phrase. In the second and third experiments, we investigated the speed at which spectrogram reading can be accomplished. In the final experiment, five students read spectrograms of normal English sentences after a 13-week course in acoustic phonetics. Working as a group, the class agreed with the transcribers on over 80% of the segments. Implications for speech recognition and aids for the deaf are discussed.

Drum Sound Recognition for Polyphonic Audio Signals by Adaptation and Matching of Spectrogram Templates With Harmonic Structure Suppression

Abstract: This paper describes a system that detects onsets of the bass drum, snare drum, and hi-hat cymbals in polyphonic audio signals of popular songs. Our system is based on a template-matching method that uses power spectrograms of drum sounds as templates. This method calculates the distance between a template and each spectrogram segment extracted from a song spectrogram, using Goto's distance measure originally designed to detect the onsets in drums-only signals. However, there are two main problems. The first problem is that appropriate templates are unknown for each song. The second problem is that it is more difficult to detect drum-sound onsets in sound mixtures including various sounds other than drum sounds. To solve these problems, we propose template-adaptation and harmonic-structure-suppression methods. First of all, an initial template of each drum sound, called a seed template, is prepared. The former method adapts it to actual drum-sound spectrograms appearing in the song spectrogram. To make our system robust to the overlapping of harmonic sounds with drum sounds, the latter method suppresses harmonic components in the song spectrogram before the adaptation and matching. Experimental results with 70 popular songs showed that our template-adaptation and harmonic-structure-suppression methods improved the recognition accuracy and achieved 83%, 58%, and 46% in detecting onsets of the bass drum, snare drum, and hi-hat cymbals, respectively

Spectrogram analysis of genomes

Abstract: We perform frequency-domain analysis in the genomes of various organisms using tricolor spectrograms, identifying several types of distinct visual patterns characterizing specific DNA regions. We relate patterns and their frequency characteristics to the sequence characteristics of the DNA. At times, the spectrogram patterns can be related to the structure of the corresponding protein region by using various public databases such as GenBank. Some patterns are explained from the biological nature of the corresponding regions, which relate to chromosome structure and protein coding, and some patterns have yet unknown biological significance. We found biologically meaningful patterns, on the scale of millions of base pairs, to a few hundred base pairs. Chromosome-wide patterns include periodicities ranging from 2 to 300. The color of the spectrogram depends on the nucleotide content at specific frequencies, and therefore can be used as a local indicator of CG content and other measures of relative base content. Several smaller-scale patterns are found to represent different types of domains made up of various tandem repeats.

Audio classification from time-frequency texture

Abstract: Time-frequency representations of audio signals often resemble texture images. This paper derives a simple audio classification algorithm based on treating sound spectrograms as texture images. The algorithm is inspired by an earlier visual classification scheme particularly efficient at classifying textures. While solely based on time-frequency texture features, the algorithm achieves surprisingly good performance in musical instrument classification experiments.

Channel-Attention Dense U-Net for Multichannel Speech Enhancement

Abstract: Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce the clean speech. Despite the great performance in the single-channel setting, these frameworks lag in performance in the multichannel setting as the majority of these methods a) fail to exploit the available spatial information fully, and b) still treat the deep architecture as a black box which may not be well-suited for multichannel audio processing. This paper addresses these drawbacks, a) by utilizing complex ratio masking instead of masking on the magnitude of the spectrogram, and more importantly, b) by introducing a channel-attention mechanism inside the deep architecture to mimic beamforming. We propose Channel-Attention Dense U-Net, in which we apply the channel-attention unit recursively on feature maps at every layer of the network, enabling the network to perform non-linear beamforming. We demonstrate the superior performance of the network against the state-of-the-art approaches on the CHiME-3 dataset.