# HG changeset patch
# User gyorgyf
# Date 1462096319 -3600
# Node ID 304aace559654e738750dadc3298b841f98bfee6
# Parent  23b8bfe481b14dbfa45f2f406ff52bf762e4c2b8
some typos

diff -r 23b8bfe481b1 -r 304aace55965 musicweb.tex
--- a/musicweb.tex	Sun May 01 10:36:35 2016 +0100
+++ b/musicweb.tex	Sun May 01 10:51:59 2016 +0100
@@ -278,9 +278,9 @@
 High-level stylistic descriptors are not easily estimated from audio but they can correlate with lower level features such as the average tempo of a track, the frequency of note onsets, the most commonly occurring keys or chords or the overall spectral envelope that characterises dominant voices or instrumentation. To exploit different types of similarity, we model each artist using three main categories of audio descriptors: rhythmic, harmonic and timbral. We compute the joint distribution of several low-level features in each category over a large collection of tracks from each artist. We then link artists exhibiting similar distributions of these features.
 
 % for XXXX artists with a mean track count of YYY
-We obtain audio features form the AcousticBrainz\footnote{https://acousticbrainz.org/} Web service which provides audio descriptors in each category of interest. Tracks are indexed by MusicBrainz identifiers enabling unambiguous linking to artists and other relevant metadata. For each artist in our database, we retrieve features for a large collection of their tracks in the above categories, including beats-per-minute and onset rate (rhythmic), chord histograms (harmonic) and MFCC (timbral) features.
+We obtain audio features form the AcousticBrainz\footnote{https://acousticbrainz.org/} Web service which provides descriptors in each category of interest. Tracks are indexed by MusicBrainz identifiers enabling unambiguous linking to artists and other relevant metadata. For each artist in our database, we retrieve features for a large collection of their tracks in the above categories, including beats-per-minute and onset rate (rhythmic), chord histograms (harmonic) and MFCC (timbral) features.
 
-For each artist, we fit a Gaussian Mixture Model (GMM) with full covariances on each set of aggregated features in each category across several tracks and compute the distances $D_{cat}$ for the selected category using Eq.\ref{eq:dist}
+For each artist, we fit a Gaussian Mixture Model (GMM) with full covariances on each set of aggregated features in each category across several tracks and compute pair-wise distances $D_{cat}$ for the selected category using Eq.\ref{eq:dist}
 %
 \begin{equation}\label{eq:dist}
 D_{cat} = d_{skl}(artist\_model_{cat}(i), artist\_model_{cat}(j)),