diff --git a/ltfatpy/fourier/dcti.py b/ltfatpy/fourier/dcti.py
index c5c0a1424b7da4d3617d2970a8b9a608134798df..de8f304803df66e3da6ff11912f06e78a80bc751 100644
--- a/ltfatpy/fourier/dcti.py
+++ b/ltfatpy/fourier/dcti.py
@@ -115,7 +115,7 @@ def dcti(f, L=None, dim=None):
.. math::
w\\left(n\\right)=\\begin{cases}\\frac{1}{\\sqrt{2}} & \\text{if }n=0
- \\text{ or }n=L-1 \\\ 1 & \\text{otherwise}\\end{cases}
+ \\text{ or }n=L-1 \\ 1 & \\text{otherwise}\\end{cases}
Then
diff --git a/ltfatpy/fourier/dctii.py b/ltfatpy/fourier/dctii.py
index 123c1fc20af05a99b3c350bbadbf569af837fafa..c8303f20a4631249175e9b505555c02b8520c929 100644
--- a/ltfatpy/fourier/dctii.py
+++ b/ltfatpy/fourier/dctii.py
@@ -107,7 +107,7 @@ def dctii(f, L=None, dim=None):
The transform is real (output is real if input is real) and
it is orthonormal.
- This is the inverse of \|dctiii\|.
+ This is the inverse of \\|dctiii\\|.
Let f be a signal of length **L**, let :math:`c=dctii(f)` and define the
vector **w** of length **L** by
@@ -117,7 +117,7 @@ def dctii(f, L=None, dim=None):
.. math::
w\\left(n\\right)=\\begin{cases}\\frac{1}{\\sqrt{2}} & \\text{if }n=0
- \\\ 1 & \\text{otherwise}\\end{cases}
+ \\ 1 & \\text{otherwise}\\end{cases}
Then
diff --git a/ltfatpy/fourier/dctiii.py b/ltfatpy/fourier/dctiii.py
index 7c0542dfc1c74437b8a12d1be5773d2ef89b8fa3..506a78df0c1334c754464ff7ec1b3b97a735c080 100644
--- a/ltfatpy/fourier/dctiii.py
+++ b/ltfatpy/fourier/dctiii.py
@@ -105,7 +105,7 @@ def dctiii(f, L=None, dim=None):
The transform is real (output is real if input is real) and
it is orthonormal.
- This is the inverse of \|dctii\|.
+ This is the inverse of \\|dctii\\|.
Let f be a signal of length **L**, let :math:`c=dctiii(f)` and define the
vector **w** of length **L** by
@@ -115,7 +115,7 @@ def dctiii(f, L=None, dim=None):
.. math::
w\\left(n\\right)=\\begin{cases}\\frac{1}{\\sqrt{2}} & \\text{if }n=0
- \\text{ or }n=L-1 \\\ 1 & \\text{otherwise}\\end{cases}
+ \\text{ or }n=L-1 \\ 1 & \\text{otherwise}\\end{cases}
Then
diff --git a/ltfatpy/fourier/dstii.py b/ltfatpy/fourier/dstii.py
index 800c544777e634ffdc82da1ab2cb9249c7d90ea7..f0348427d20b38cd0681fac5345f73dc2dd22eee 100644
--- a/ltfatpy/fourier/dstii.py
+++ b/ltfatpy/fourier/dstii.py
@@ -103,7 +103,7 @@ def dstii(f, L=None, dim=None):
The transform is real (output is real if input is real) and orthonormal.
- The inverse transform of \\|dstii\| is \\|dstiii\|.
+ The inverse transform of \\|dstii\\| is \\|dstiii\\|.
Let **f** be a signal of length **L**, let ``c=dstii(f)`` and define the
vector **w** of length **L** by
@@ -113,7 +113,7 @@ def dstii(f, L=None, dim=None):
.. math::
w\\left(n\\right)=\\begin{cases}\\frac{1}{\\sqrt{2}} &
- \\text{if }n=L-1 \\\ 1 & \\text{otherwise}\\end{cases}
+ \\text{if }n=L-1 \\ 1 & \\text{otherwise}\\end{cases}
Then
diff --git a/ltfatpy/fourier/dstiii.py b/ltfatpy/fourier/dstiii.py
index 0a0359e4e9e9db2e36e3b5edadab3e32be1ea35d..a0ce139a23ba5f2fe65867171da4a4a7fc57e314 100644
--- a/ltfatpy/fourier/dstiii.py
+++ b/ltfatpy/fourier/dstiii.py
@@ -113,7 +113,7 @@ def dstiii(f, L=None, dim=None):
.. math::
w\\left(n\\right)=\\begin{cases}\\frac{1}{\\sqrt{2}} &
- \\text{if }n=L-1 \\\ 1 & \\text{otherwise}\\end{cases}
+ \\text{if }n=L-1 \\ 1 & \\text{otherwise}\\end{cases}
Then
diff --git a/ltfatpy/fourier/psech.py b/ltfatpy/fourier/psech.py
index 58cfd3cc0f1f572de8e53720effd92e5c6da8c00..aff6dc9fa8080115dece2fc183f78e5a0ea97019 100644
--- a/ltfatpy/fourier/psech.py
+++ b/ltfatpy/fourier/psech.py
@@ -91,7 +91,7 @@ def psech(L, tfr=None, s=None, **kwargs):
``psech(L,tfr)`` computes samples of a periodized hyperbolic secant.
The function returns a regular sampling of the periodization
- of the function :math:`sech(\pi\cdot x)`
+ of the function :math:`sech(\\pi\\cdot x)`
The returned function has norm equal to 1.
diff --git a/ltfatpy/gabor/dgt.py b/ltfatpy/gabor/dgt.py
index 8be57a9c226a2f52f08e4f0df5b45b123aedf8f2..1f01630d9dc9da1ad31fc7aa9966e171d015d414 100644
--- a/ltfatpy/gabor/dgt.py
+++ b/ltfatpy/gabor/dgt.py
@@ -173,7 +173,7 @@ def dgt(f, g, a, M, L=None, pt='freqinv'):
c\\left(m+1,n+1\\right)=\\sum_{l=0}^{L-1}f(l+1)\\overline{g(l-an+1)}
e^{-2\\pi ilm/M}
- where :math:`m=0,\ldots,M-1`, :math:`n=0, \\ldots,N-1` and :math:`l-an`
+ where :math:`m=0,\\ldots,M-1`, :math:`n=0, \\ldots,N-1` and :math:`l-an`
are computed modulo **L**.
- Additional parameters:
diff --git a/ltfatpy/gabor/idgt.py b/ltfatpy/gabor/idgt.py
index df7da923da9a08640aeb650bf8de4da60dc79672..35f8cb1a1a185e6d4a916aa551de59b23b687501 100644
--- a/ltfatpy/gabor/idgt.py
+++ b/ltfatpy/gabor/idgt.py
@@ -117,7 +117,7 @@ def idgt(coef, g, a, Ls=None, pt='freqinv'):
one column vector for each of the TF-planes in **c**.
Assume that ``f=idgt(c, g, a, L)`` for an array **c** of size
- :math:`M \times N`. Then the following holds for :math:`k=0,\ldots,L-1`:
+ :math:`M \\times N`. Then the following holds for :math:`k=0,\\ldots,L-1`:
.. math::
diff --git a/ltfatpy/gabor/phaselock.py b/ltfatpy/gabor/phaselock.py
index 5e5d5443cdf0ace9a63fa2d2cf8034b95c3459f1..5a4a47291c08563c6d7b46b8a6fda7f1dbcc0f52 100644
--- a/ltfatpy/gabor/phaselock.py
+++ b/ltfatpy/gabor/phaselock.py
@@ -103,8 +103,8 @@ def phaselock(c, a):
c(m+1,n+1) = sum f(l+1)*exp(-2*pi*i*m*(l-n*a)/M)*conj(g(l-a*n+1)),
l=0
- .. math:: c\\left(m+1,n+1\\right)=\sum_{l=0}^{L-1}f(l+1)
- e^{-2\pi im(l-na)/M}\overline{g(l-an+1)},
+ .. math:: c\\left(m+1,n+1\\right)=\\sum_{l=0}^{L-1}f(l+1)
+ e^{-2\\pi im(l-na)/M}\\overline{g(l-an+1)},
where ``m = 0,..., M-1`` and ``n = 0,..., N-1`` and ``l-a*n`` are computed
modulo ``L``.
diff --git a/ltfatpy/gabor/tfplot.py b/ltfatpy/gabor/tfplot.py
index a5aa845e57df12a53526bdaeb7f8a231dbea68ee..670d054de7da55398a33f83dadea6999451ae85e 100644
--- a/ltfatpy/gabor/tfplot.py
+++ b/ltfatpy/gabor/tfplot.py
@@ -123,12 +123,12 @@ def tfplot(coef, step, yr, fs=None, dynrange=None, normalization='db',
Possible values for **normalization**:
============ ==========================================================
- ``'db'`` Apply :math:`20*\log_{10}` to the coefficients. This makes
+ ``'db'`` Apply :math:`20*\\log_{10}` to the coefficients. This makes
it possible to see very weak phenomena, but it might show
too much noise. A logarithmic scale is more adapted to
perception of sound. This is the default.
- ``'dbsq'`` Apply :math:`10*\log_{10}` to the coefficients. Same as
+ ``'dbsq'`` Apply :math:`10*\\log_{10}` to the coefficients. Same as
the ``'db'`` option, but assume that the input is already
squared.
diff --git a/ltfatpy/sigproc/normalize.py b/ltfatpy/sigproc/normalize.py
index 5c4b08958b1cfe13364ee3642d4ed747fd1eb9e6..7d2971dea4a2fa4fcc65c6a6d146a43d759b9486 100644
--- a/ltfatpy/sigproc/normalize.py
+++ b/ltfatpy/sigproc/normalize.py
@@ -145,10 +145,10 @@ def normalize(f, norm='2', dim=None):
fnorm[ii] = LA.norm(f[:, ii], np.inf)
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == 'rms':
- fnorm[ii] = rms(f[:, ii])
+ fnorm[ii] = rms(f[:, ii]).item()
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == 's0':
- fnorm[ii] = s0norm(f[:, ii])
+ fnorm[ii] = s0norm(f[:, ii]).item()
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == 'wav':
if np.issubdtype(f.dtype, np.floating):