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<< Week 6: Challenges to Music Curation Week 8: Analysing and Extracting Meaning from Audio >>

Week 7: Music as Sound

Task 1: Identifying Tracks and Creating a Metadata Table

Title Artist Composer Copyright Genre Source Audio Format Number of Channels Sample Rate Bits/Sample Duration
Lamentate VII.Stridendo Maki Namekawa Arvo Pärt CC-BY-NC Contemporary Classical Spotify .WAV 2 48 kHz 24 02:01
Pro et contra: III.Allegro Frans Helmerson Arvo Pärt CC-BY-NC Contemporary Classical Spotify .WAV 2 48 kHz 24 03:13
Drei Hirtenkinder aus Fátima Johannes Stecher Arvo Pärt CC-BY-NC Contemporary Classical Spotify .WAV 2 48 kHz 24 01:37

Task 2.1: Basic Analysis of Tracks in SonicVisualizer

Note: to see the visualisation in full, right-click and open the image in a new tab where you can zoom in.

Lamentate VII.Stridendo by Maki Namekawa

Waveform of Lamentate VII.Stridendo by Maki Namekawa Spectrogram of Lamentate VII.Stridendo by Maki Namekawa

Pro et contra: III.Allegro by Frans Helmerson

Waveform of Pro et contra: III.Allegro by Frans Helmerson Spectrogram of Pro et contra: III.Allegro by Frans Helmerson

Drei Hirtenkinder aus Fátima by Johannes Stecher

Waveform of Drei Hirtenkinder aus Fátima by Johannes Stecher Spectrogram of Drei Hirtenkinder aus Fátima by Johannes Stecher

Task 2.2: Advantage of a Time-Frequency Analysis Over a Waveform-Based Analysis

Using spectrographic visualisation techniques in Sonic Visualiser, users can distinguish specific frequency components at specific points in time, providing a comprehensive image of the sound representation. The waveform is inferior to this. For music transcription, it is important that time-frequency analysis simultaneously detects frequency content at different time intervals. Therefore, it is crucial. With spectrography, dynamic pitch changes and frequency modulation can be studied better and more accurately.